Instructions to use werner1hugo/lid-framework-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use werner1hugo/lid-framework-models with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("werner1hugo/lid-framework-models", "model.bin")) - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse files- fasttext/amazon_reviews_multi/fasttext_subword/classification_report.txt +30 -0
- fasttext/amazon_reviews_multi/fasttext_subword/param_search_results.csv +10 -0
- fasttext/amazon_reviews_multi/fasttext_subword/run_metadata.json +24 -0
- fasttext/amazon_reviews_multi/fasttext_word/classification_report.txt +30 -0
- fasttext/amazon_reviews_multi/fasttext_word/param_search_results.csv +10 -0
- fasttext/amazon_reviews_multi/fasttext_word/run_metadata.json +24 -0
- fasttext/europarl/fasttext_subword/classification_report.txt +30 -0
- fasttext/europarl/fasttext_subword/param_search_results.csv +10 -0
- fasttext/europarl/fasttext_subword/run_metadata.json +24 -0
- fasttext/europarl/fasttext_word/classification_report.txt +30 -0
- fasttext/europarl/fasttext_word/param_search_results.csv +10 -0
- fasttext/europarl/fasttext_word/run_metadata.json +24 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_subword/classification_report.txt +30 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_subword/param_search_results.csv +10 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_subword/run_metadata.json +24 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_word/classification_report.txt +30 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_word/param_search_results.csv +10 -0
- fasttext/exorde-social-media-december-2024-week1/fasttext_word/run_metadata.json +24 -0
- fasttext/flores_plus/fasttext_subword/classification_report.txt +30 -0
- fasttext/flores_plus/fasttext_subword/param_search_results.csv +10 -0
- fasttext/flores_plus/fasttext_subword/run_metadata.json +24 -0
- fasttext/flores_plus/fasttext_word/classification_report.txt +30 -0
- fasttext/flores_plus/fasttext_word/param_search_results.csv +10 -0
- fasttext/flores_plus/fasttext_word/run_metadata.json +24 -0
- fasttext/language-identification/fasttext_subword/classification_report.txt +30 -0
- fasttext/language-identification/fasttext_subword/param_search_results.csv +10 -0
- fasttext/language-identification/fasttext_subword/run_metadata.json +24 -0
- fasttext/language-identification/fasttext_word/classification_report.txt +30 -0
- fasttext/language-identification/fasttext_word/param_search_results.csv +10 -0
- fasttext/language-identification/fasttext_word/run_metadata.json +24 -0
- fasttext/massive/fasttext_subword/classification_report.txt +30 -0
- fasttext/massive/fasttext_subword/param_search_results.csv +10 -0
- fasttext/massive/fasttext_subword/run_metadata.json +24 -0
- fasttext/massive/fasttext_word/classification_report.txt +30 -0
- fasttext/massive/fasttext_word/param_search_results.csv +10 -0
- fasttext/massive/fasttext_word/run_metadata.json +24 -0
- fasttext/mmarco/fasttext_subword/classification_report.txt +30 -0
- fasttext/mmarco/fasttext_subword/param_search_results.csv +10 -0
- fasttext/mmarco/fasttext_subword/run_metadata.json +24 -0
- fasttext/mmarco/fasttext_word/classification_report.txt +30 -0
- fasttext/mmarco/fasttext_word/param_search_results.csv +10 -0
- fasttext/mmarco/fasttext_word/run_metadata.json +24 -0
- fasttext/multilingual_cc_news/fasttext_subword/classification_report.txt +30 -0
- fasttext/multilingual_cc_news/fasttext_subword/param_search_results.csv +10 -0
- fasttext/multilingual_cc_news/fasttext_subword/run_metadata.json +24 -0
- fasttext/multilingual_cc_news/fasttext_word/classification_report.txt +30 -0
- fasttext/multilingual_cc_news/fasttext_word/param_search_results.csv +10 -0
- fasttext/multilingual_cc_news/fasttext_word/run_metadata.json +24 -0
- fasttext/multilingual_toxicity_dataset/fasttext_subword/classification_report.txt +30 -0
- fasttext/multilingual_toxicity_dataset/fasttext_subword/param_search_results.csv +10 -0
fasttext/amazon_reviews_multi/fasttext_subword/classification_report.txt
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precision recall f1-score support
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fr 1.00 1.00 1.00 19900
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ja 1.00 1.00 1.00 19990
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ko 0.00 0.00 0.00 0
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lt 0.00 0.00 0.00 0
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uk 0.00 0.00 0.00 0
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zh 1.00 1.00 1.00 19993
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accuracy 1.00 119541
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macro avg 0.25 0.25 0.25 119541
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weighted avg 1.00 1.00 1.00 119541
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fasttext/amazon_reviews_multi/fasttext_subword/param_search_results.csv
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epoch,lr,val_f1_macro,train_time_s
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5,0.05,0.999603,11.32
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5,0.1,0.999665,11.4
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5,0.5,0.999833,11.29
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10,0.05,0.999707,19.9
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10,0.1,0.999791,19.93
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10,0.5,0.999833,19.65
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25,0.05,0.999833,44.87
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25,0.1,0.999812,45.65
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25,0.5,0.999854,45.07
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fasttext/amazon_reviews_multi/fasttext_subword/run_metadata.json
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{
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"run_name": "fasttext_subword",
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"tags": {
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"feature_type": "subword",
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"wordNgrams": "2",
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"minn": "2",
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"maxn": "4",
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"loss": "softmax",
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"dataset_version": "amazon_reviews_multi"
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},
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"params": {
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"epoch": "25",
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"lr": "0.5"
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},
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"metrics": {
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"accuracy": 0.999832693385533,
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"f1_macro": 0.9998326634729101,
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"precision_macro": 0.9998323692172443,
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"recall_macro": 0.9998329695159586,
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"inference_time_total_s": 2.9018,
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"inference_time_ms_per_sample": 0.024275,
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"throughput_samples_per_sec": 41195.4
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}
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}
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fasttext/amazon_reviews_multi/fasttext_word/classification_report.txt
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precision recall f1-score support
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ca 0.00 0.00 0.00 0
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da 0.00 0.00 0.00 0
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de 1.00 1.00 1.00 20022
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el 0.00 0.00 0.00 0
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en 1.00 1.00 1.00 19795
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es 1.00 1.00 1.00 19841
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fi 0.00 0.00 0.00 0
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fr 1.00 1.00 1.00 19900
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hr 0.00 0.00 0.00 0
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it 0.00 0.00 0.00 0
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ja 0.94 0.06 0.11 19990
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ko 0.00 0.00 0.00 0
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lt 0.00 0.00 0.00 0
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mk 0.00 0.00 0.00 0
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nb 0.00 0.00 0.00 0
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nl 0.00 0.00 0.00 0
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pl 0.00 0.00 0.00 0
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sv 0.00 0.00 0.00 0
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uk 0.00 0.00 0.00 0
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zh 0.51 1.00 0.68 19993
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accuracy 0.84 119541
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| 29 |
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macro avg 0.23 0.21 0.20 119541
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weighted avg 0.91 0.84 0.80 119541
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fasttext/amazon_reviews_multi/fasttext_word/param_search_results.csv
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epoch,lr,val_f1_macro,train_time_s
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5,0.05,0.797777,4.5
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5,0.1,0.797788,4.55
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5,0.5,0.797734,4.81
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10,0.05,0.797729,7.3
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10,0.1,0.797818,7.42
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10,0.5,0.797837,7.14
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25,0.05,0.797636,18.96
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25,0.1,0.797611,17.49
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25,0.5,0.79789,20.36
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fasttext/amazon_reviews_multi/fasttext_word/run_metadata.json
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{
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"run_name": "fasttext_word",
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"tags": {
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"feature_type": "word_ngram",
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"wordNgrams": "1",
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"minn": "0",
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"maxn": "0",
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"loss": "softmax",
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"dataset_version": "amazon_reviews_multi"
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},
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"params": {
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"epoch": "25",
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"lr": "0.5"
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},
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| 15 |
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"metrics": {
|
| 16 |
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"accuracy": 0.8411674655557507,
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| 17 |
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"f1_macro": 0.7968306004142348,
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| 18 |
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"precision_macro": 0.9092107304710257,
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| 19 |
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"recall_macro": 0.8416939311995351,
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| 20 |
+
"inference_time_total_s": 0.8704,
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| 21 |
+
"inference_time_ms_per_sample": 0.007281,
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| 22 |
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"throughput_samples_per_sec": 137346.04
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| 23 |
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}
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}
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fasttext/europarl/fasttext_subword/classification_report.txt
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precision recall f1-score support
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| 2 |
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ca 0.00 0.00 0.00 0
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da 1.00 1.00 1.00 19996
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de 1.00 1.00 1.00 20016
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el 1.00 1.00 1.00 19956
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en 1.00 1.00 1.00 20082
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es 1.00 1.00 1.00 19992
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| 9 |
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fi 1.00 1.00 1.00 19935
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| 10 |
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fr 1.00 1.00 1.00 19942
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| 11 |
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hr 0.00 0.00 0.00 0
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| 12 |
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it 1.00 1.00 1.00 20036
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| 13 |
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ja 0.00 0.00 0.00 0
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| 14 |
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ko 0.00 0.00 0.00 0
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lt 0.00 0.00 0.00 0
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mk 0.00 0.00 0.00 0
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nb 0.00 0.00 0.00 0
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| 18 |
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nl 1.00 1.00 1.00 20086
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| 19 |
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pl 1.00 1.00 1.00 20006
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| 20 |
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pt 1.00 1.00 1.00 19998
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ro 1.00 1.00 1.00 19916
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ru 0.00 0.00 0.00 0
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sl 0.00 0.00 0.00 0
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| 24 |
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sv 1.00 1.00 1.00 19996
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uk 0.00 0.00 0.00 0
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| 26 |
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zh 0.00 0.00 0.00 0
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| 27 |
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| 28 |
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accuracy 1.00 259957
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| 29 |
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macro avg 0.54 0.54 0.54 259957
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| 30 |
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weighted avg 1.00 1.00 1.00 259957
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fasttext/europarl/fasttext_subword/param_search_results.csv
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epoch,lr,val_f1_macro,train_time_s
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5,0.05,0.999106,28.6
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5,0.1,0.999317,28.82
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| 4 |
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5,0.5,0.999471,28.87
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| 5 |
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10,0.05,0.999298,54.14
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| 6 |
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10,0.1,0.999375,54.15
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| 7 |
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10,0.5,0.9995,54.14
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| 8 |
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25,0.05,0.999385,129.2
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| 9 |
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25,0.1,0.999462,128.75
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| 10 |
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25,0.5,0.999519,129.88
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fasttext/europarl/fasttext_subword/run_metadata.json
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{
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"run_name": "fasttext_subword",
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| 3 |
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"tags": {
|
| 4 |
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"feature_type": "subword",
|
| 5 |
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"wordNgrams": "2",
|
| 6 |
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"minn": "2",
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| 7 |
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"maxn": "4",
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| 8 |
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"loss": "softmax",
|
| 9 |
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"dataset_version": "europarl"
|
| 10 |
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},
|
| 11 |
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"params": {
|
| 12 |
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"epoch": "25",
|
| 13 |
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"lr": "0.5"
|
| 14 |
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},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9993729732224945,
|
| 17 |
+
"f1_macro": 0.9993729867417601,
|
| 18 |
+
"precision_macro": 0.9993731983961379,
|
| 19 |
+
"recall_macro": 0.9993728972464199,
|
| 20 |
+
"inference_time_total_s": 6.7203,
|
| 21 |
+
"inference_time_ms_per_sample": 0.025852,
|
| 22 |
+
"throughput_samples_per_sec": 38682.27
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/europarl/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 1.00 1.00 1.00 19996
|
| 5 |
+
de 1.00 1.00 1.00 20016
|
| 6 |
+
el 1.00 1.00 1.00 19956
|
| 7 |
+
en 1.00 1.00 1.00 20082
|
| 8 |
+
es 1.00 1.00 1.00 19992
|
| 9 |
+
fi 0.99 1.00 1.00 19935
|
| 10 |
+
fr 1.00 1.00 1.00 19942
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 1.00 1.00 1.00 20036
|
| 13 |
+
ja 0.00 0.00 0.00 0
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 1.00 1.00 1.00 20086
|
| 19 |
+
pl 1.00 1.00 1.00 20006
|
| 20 |
+
pt 1.00 1.00 1.00 19998
|
| 21 |
+
ro 1.00 1.00 1.00 19916
|
| 22 |
+
ru 0.00 0.00 0.00 0
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 1.00 1.00 1.00 19996
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 0.00 0.00 0.00 0
|
| 27 |
+
|
| 28 |
+
accuracy 1.00 259957
|
| 29 |
+
macro avg 0.54 0.54 0.54 259957
|
| 30 |
+
weighted avg 1.00 1.00 1.00 259957
|
fasttext/europarl/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.998269,14.95
|
| 3 |
+
5,0.1,0.998519,15.2
|
| 4 |
+
5,0.5,0.998721,15.08
|
| 5 |
+
10,0.05,0.998451,26.6
|
| 6 |
+
10,0.1,0.998557,28.96
|
| 7 |
+
10,0.5,0.998576,25.75
|
| 8 |
+
25,0.05,0.998586,62.89
|
| 9 |
+
25,0.1,0.998702,65.83
|
| 10 |
+
25,0.5,0.998759,63.87
|
fasttext/europarl/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "europarl"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9985997684232393,
|
| 17 |
+
"f1_macro": 0.9985992859536525,
|
| 18 |
+
"precision_macro": 0.9986004248910461,
|
| 19 |
+
"recall_macro": 0.9985994130953136,
|
| 20 |
+
"inference_time_total_s": 2.4701,
|
| 21 |
+
"inference_time_ms_per_sample": 0.009502,
|
| 22 |
+
"throughput_samples_per_sec": 105243.52
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/exorde-social-media-december-2024-week1/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.96 0.94 0.95 3501
|
| 4 |
+
da 0.91 0.89 0.90 3175
|
| 5 |
+
de 0.97 0.97 0.97 19988
|
| 6 |
+
el 1.00 0.99 0.99 2671
|
| 7 |
+
en 0.94 0.96 0.95 19955
|
| 8 |
+
es 0.98 0.98 0.98 20014
|
| 9 |
+
fi 0.91 0.93 0.92 4341
|
| 10 |
+
fr 0.99 0.99 0.99 19964
|
| 11 |
+
hr 0.94 0.93 0.93 1723
|
| 12 |
+
it 0.97 0.97 0.97 19940
|
| 13 |
+
ja 0.97 0.97 0.97 19926
|
| 14 |
+
ko 0.98 0.97 0.97 9596
|
| 15 |
+
lt 0.96 0.90 0.93 385
|
| 16 |
+
mk 0.99 0.94 0.97 369
|
| 17 |
+
nb 0.85 0.82 0.84 1657
|
| 18 |
+
nl 0.98 0.97 0.98 10778
|
| 19 |
+
pl 0.97 0.97 0.97 7536
|
| 20 |
+
pt 0.98 0.99 0.99 19948
|
| 21 |
+
ro 0.97 0.96 0.97 2756
|
| 22 |
+
ru 0.98 0.98 0.98 19824
|
| 23 |
+
sl 0.84 0.77 0.80 713
|
| 24 |
+
sv 0.97 0.95 0.96 6856
|
| 25 |
+
uk 0.97 0.93 0.95 4517
|
| 26 |
+
zh 0.95 0.96 0.96 19968
|
| 27 |
+
|
| 28 |
+
accuracy 0.97 240101
|
| 29 |
+
macro avg 0.96 0.94 0.95 240101
|
| 30 |
+
weighted avg 0.97 0.97 0.97 240101
|
fasttext/exorde-social-media-december-2024-week1/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.905079,28.26
|
| 3 |
+
5,0.1,0.94221,28.16
|
| 4 |
+
5,0.5,0.949372,28.39
|
| 5 |
+
10,0.05,0.942726,49.96
|
| 6 |
+
10,0.1,0.948625,50.42
|
| 7 |
+
10,0.5,0.949611,49.85
|
| 8 |
+
25,0.05,0.949354,113.56
|
| 9 |
+
25,0.1,0.948747,113.67
|
| 10 |
+
25,0.5,0.949866,113.26
|
fasttext/exorde-social-media-december-2024-week1/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "exorde-social-media-december-2024-week1"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9682342014402272,
|
| 17 |
+
"f1_macro": 0.9491932783934312,
|
| 18 |
+
"precision_macro": 0.9559232010596661,
|
| 19 |
+
"recall_macro": 0.9428664563019996,
|
| 20 |
+
"inference_time_total_s": 7.319,
|
| 21 |
+
"inference_time_ms_per_sample": 0.030483,
|
| 22 |
+
"throughput_samples_per_sec": 32805.16
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/exorde-social-media-december-2024-week1/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.95 0.92 0.93 3501
|
| 4 |
+
da 0.89 0.87 0.88 3175
|
| 5 |
+
de 0.96 0.95 0.96 19988
|
| 6 |
+
el 1.00 0.97 0.98 2671
|
| 7 |
+
en 0.92 0.95 0.94 19955
|
| 8 |
+
es 0.97 0.97 0.97 20014
|
| 9 |
+
fi 0.88 0.82 0.85 4341
|
| 10 |
+
fr 0.99 0.99 0.99 19964
|
| 11 |
+
hr 0.93 0.90 0.92 1723
|
| 12 |
+
it 0.97 0.96 0.96 19940
|
| 13 |
+
ja 0.56 0.95 0.70 19926
|
| 14 |
+
ko 0.97 0.86 0.91 9596
|
| 15 |
+
lt 0.95 0.82 0.88 385
|
| 16 |
+
mk 0.97 0.91 0.94 369
|
| 17 |
+
nb 0.84 0.76 0.80 1657
|
| 18 |
+
nl 0.98 0.96 0.97 10778
|
| 19 |
+
pl 0.96 0.95 0.95 7536
|
| 20 |
+
pt 0.98 0.98 0.98 19948
|
| 21 |
+
ro 0.96 0.95 0.96 2756
|
| 22 |
+
ru 0.97 0.96 0.97 19824
|
| 23 |
+
sl 0.82 0.71 0.76 713
|
| 24 |
+
sv 0.96 0.95 0.95 6856
|
| 25 |
+
uk 0.97 0.86 0.91 4517
|
| 26 |
+
zh 0.81 0.37 0.51 19968
|
| 27 |
+
|
| 28 |
+
accuracy 0.90 240101
|
| 29 |
+
macro avg 0.92 0.89 0.90 240101
|
| 30 |
+
weighted avg 0.92 0.90 0.90 240101
|
fasttext/exorde-social-media-december-2024-week1/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.894855,20.1
|
| 3 |
+
5,0.1,0.898782,19.43
|
| 4 |
+
5,0.5,0.899593,19.6
|
| 5 |
+
10,0.05,0.898107,35.54
|
| 6 |
+
10,0.1,0.899117,36.32
|
| 7 |
+
10,0.5,0.899015,36.91
|
| 8 |
+
25,0.05,0.89845,85.09
|
| 9 |
+
25,0.1,0.899848,82.77
|
| 10 |
+
25,0.5,0.898883,87.97
|
fasttext/exorde-social-media-december-2024-week1/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "exorde-social-media-december-2024-week1"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.1"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.8991632687910505,
|
| 17 |
+
"f1_macro": 0.8985305506686693,
|
| 18 |
+
"precision_macro": 0.9231642533621339,
|
| 19 |
+
"recall_macro": 0.8866139538959009,
|
| 20 |
+
"inference_time_total_s": 2.8393,
|
| 21 |
+
"inference_time_ms_per_sample": 0.011825,
|
| 22 |
+
"throughput_samples_per_sec": 84564.44
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/flores_plus/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.99 0.97 0.98 80
|
| 4 |
+
da 0.90 0.84 0.87 94
|
| 5 |
+
de 0.98 0.98 0.98 61
|
| 6 |
+
el 1.00 1.00 1.00 62
|
| 7 |
+
en 1.00 1.00 1.00 94
|
| 8 |
+
es 0.98 0.97 0.97 58
|
| 9 |
+
fi 0.99 1.00 0.99 84
|
| 10 |
+
fr 0.96 0.98 0.97 55
|
| 11 |
+
hr 0.97 0.98 0.97 98
|
| 12 |
+
it 1.00 1.00 1.00 55
|
| 13 |
+
ja 0.99 0.99 0.99 199
|
| 14 |
+
ko 1.00 1.00 1.00 205
|
| 15 |
+
lt 0.98 1.00 0.99 91
|
| 16 |
+
mk 1.00 0.98 0.99 81
|
| 17 |
+
nb 0.86 0.92 0.89 103
|
| 18 |
+
nl 0.99 0.97 0.98 72
|
| 19 |
+
pl 1.00 1.00 1.00 83
|
| 20 |
+
pt 0.98 0.99 0.98 80
|
| 21 |
+
ro 1.00 0.99 0.99 69
|
| 22 |
+
ru 0.98 1.00 0.99 83
|
| 23 |
+
sl 0.99 0.96 0.97 100
|
| 24 |
+
sv 0.99 0.99 0.99 98
|
| 25 |
+
uk 1.00 1.00 1.00 87
|
| 26 |
+
zh 0.00 0.00 0.00 0
|
| 27 |
+
|
| 28 |
+
accuracy 0.98 2092
|
| 29 |
+
macro avg 0.94 0.94 0.94 2092
|
| 30 |
+
weighted avg 0.98 0.98 0.98 2092
|
fasttext/flores_plus/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.148222,0.72
|
| 3 |
+
5,0.1,0.052794,0.73
|
| 4 |
+
5,0.5,0.839754,0.73
|
| 5 |
+
10,0.05,0.052794,0.83
|
| 6 |
+
10,0.1,0.071336,0.83
|
| 7 |
+
10,0.5,0.961842,0.83
|
| 8 |
+
25,0.05,0.200145,1.33
|
| 9 |
+
25,0.1,0.828753,1.23
|
| 10 |
+
25,0.5,0.974251,1.34
|
fasttext/flores_plus/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "flores_plus"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9789674952198852,
|
| 17 |
+
"f1_macro": 0.9787420891328592,
|
| 18 |
+
"precision_macro": 0.9791241929467931,
|
| 19 |
+
"recall_macro": 0.9786400130801987,
|
| 20 |
+
"inference_time_total_s": 0.0755,
|
| 21 |
+
"inference_time_ms_per_sample": 0.036074,
|
| 22 |
+
"throughput_samples_per_sec": 27720.71
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/flores_plus/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.97 0.94 0.96 80
|
| 4 |
+
da 0.85 0.87 0.86 94
|
| 5 |
+
de 1.00 0.98 0.99 61
|
| 6 |
+
el 1.00 1.00 1.00 62
|
| 7 |
+
en 0.99 0.97 0.98 94
|
| 8 |
+
es 0.95 0.97 0.96 58
|
| 9 |
+
fi 0.99 0.88 0.93 84
|
| 10 |
+
fr 1.00 0.96 0.98 55
|
| 11 |
+
hr 0.92 0.89 0.90 98
|
| 12 |
+
it 0.96 0.98 0.97 55
|
| 13 |
+
ja 0.73 0.99 0.84 199
|
| 14 |
+
ko 1.00 0.84 0.91 205
|
| 15 |
+
lt 1.00 0.92 0.96 91
|
| 16 |
+
mk 1.00 0.98 0.99 81
|
| 17 |
+
nb 0.88 0.82 0.85 103
|
| 18 |
+
nl 0.95 0.99 0.97 72
|
| 19 |
+
pl 0.93 0.90 0.91 83
|
| 20 |
+
pt 0.99 0.99 0.99 80
|
| 21 |
+
ro 0.97 0.93 0.95 69
|
| 22 |
+
ru 0.94 0.89 0.91 83
|
| 23 |
+
sl 0.97 0.93 0.95 100
|
| 24 |
+
sv 0.94 0.96 0.95 98
|
| 25 |
+
uk 0.97 0.95 0.96 87
|
| 26 |
+
zh 0.00 0.00 0.00 0
|
| 27 |
+
|
| 28 |
+
accuracy 0.93 2092
|
| 29 |
+
macro avg 0.91 0.90 0.90 2092
|
| 30 |
+
weighted avg 0.94 0.93 0.93 2092
|
fasttext/flores_plus/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.033162,0.21
|
| 3 |
+
5,0.1,0.255358,0.31
|
| 4 |
+
5,0.5,0.952137,0.21
|
| 5 |
+
10,0.05,0.26373,0.41
|
| 6 |
+
10,0.1,0.886777,0.31
|
| 7 |
+
10,0.5,0.96238,0.31
|
| 8 |
+
25,0.05,0.920455,0.71
|
| 9 |
+
25,0.1,0.954555,0.61
|
| 10 |
+
25,0.5,0.960734,0.61
|
fasttext/flores_plus/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "flores_plus"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "10",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9292543021032504,
|
| 17 |
+
"f1_macro": 0.9418987015368867,
|
| 18 |
+
"precision_macro": 0.9510548514899856,
|
| 19 |
+
"recall_macro": 0.9360289085752775,
|
| 20 |
+
"inference_time_total_s": 0.0252,
|
| 21 |
+
"inference_time_ms_per_sample": 0.012046,
|
| 22 |
+
"throughput_samples_per_sec": 83016.1
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/language-identification/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.00 0.00 0.00 0
|
| 5 |
+
de 1.00 1.00 1.00 433
|
| 6 |
+
el 1.00 1.00 1.00 418
|
| 7 |
+
en 0.99 1.00 1.00 441
|
| 8 |
+
es 1.00 0.99 0.99 436
|
| 9 |
+
fi 0.00 0.00 0.00 0
|
| 10 |
+
fr 1.00 1.00 1.00 417
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 0.99 0.99 0.99 428
|
| 13 |
+
ja 1.00 1.00 1.00 449
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 0.99 0.99 0.99 399
|
| 19 |
+
pl 1.00 0.98 0.99 392
|
| 20 |
+
pt 0.98 0.99 0.99 423
|
| 21 |
+
ro 0.00 0.00 0.00 0
|
| 22 |
+
ru 1.00 1.00 1.00 425
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 0.00 0.00 0.00 0
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 0.99 1.00 1.00 452
|
| 27 |
+
|
| 28 |
+
accuracy 1.00 5113
|
| 29 |
+
macro avg 0.50 0.50 0.50 5113
|
| 30 |
+
weighted avg 1.00 1.00 1.00 5113
|
fasttext/language-identification/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.356591,0.88
|
| 3 |
+
5,0.1,0.875076,0.9
|
| 4 |
+
5,0.5,0.997567,0.89
|
| 5 |
+
10,0.05,0.876053,1.19
|
| 6 |
+
10,0.1,0.996574,1.19
|
| 7 |
+
10,0.5,0.997567,1.19
|
| 8 |
+
25,0.05,0.996574,2.19
|
| 9 |
+
25,0.1,0.997567,2.1
|
| 10 |
+
25,0.5,0.998513,2.19
|
fasttext/language-identification/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "language-identification"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9951105026403285,
|
| 17 |
+
"f1_macro": 0.9950397893392807,
|
| 18 |
+
"precision_macro": 0.9951121403974614,
|
| 19 |
+
"recall_macro": 0.9949879324949041,
|
| 20 |
+
"inference_time_total_s": 0.1392,
|
| 21 |
+
"inference_time_ms_per_sample": 0.027218,
|
| 22 |
+
"throughput_samples_per_sec": 36740.42
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/language-identification/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.00 0.00 0.00 0
|
| 5 |
+
de 1.00 0.99 1.00 433
|
| 6 |
+
el 1.00 1.00 1.00 418
|
| 7 |
+
en 1.00 1.00 1.00 441
|
| 8 |
+
es 1.00 0.99 0.99 436
|
| 9 |
+
fi 0.00 0.00 0.00 0
|
| 10 |
+
fr 0.99 1.00 0.99 417
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 0.99 0.98 0.98 428
|
| 13 |
+
ja 0.55 0.01 0.03 449
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 0.98 0.95 0.96 399
|
| 19 |
+
pl 0.98 0.93 0.96 392
|
| 20 |
+
pt 0.98 0.97 0.97 423
|
| 21 |
+
ro 0.00 0.00 0.00 0
|
| 22 |
+
ru 1.00 0.98 0.99 425
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 0.00 0.00 0.00 0
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 0.48 1.00 0.65 452
|
| 27 |
+
|
| 28 |
+
accuracy 0.90 5113
|
| 29 |
+
macro avg 0.46 0.45 0.44 5113
|
| 30 |
+
weighted avg 0.91 0.90 0.87 5113
|
fasttext/language-identification/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.860653,0.31
|
| 3 |
+
5,0.1,0.871508,0.32
|
| 4 |
+
5,0.5,0.876016,0.32
|
| 5 |
+
10,0.05,0.871487,0.52
|
| 6 |
+
10,0.1,0.872706,0.52
|
| 7 |
+
10,0.5,0.877418,0.52
|
| 8 |
+
25,0.05,0.874294,1.02
|
| 9 |
+
25,0.1,0.875165,1.22
|
| 10 |
+
25,0.5,0.876685,1.12
|
fasttext/language-identification/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "language-identification"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "10",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.8951691766086446,
|
| 17 |
+
"f1_macro": 0.8759973255488999,
|
| 18 |
+
"precision_macro": 0.910714604186456,
|
| 19 |
+
"recall_macro": 0.8988870618046478,
|
| 20 |
+
"inference_time_total_s": 0.0438,
|
| 21 |
+
"inference_time_ms_per_sample": 0.008571,
|
| 22 |
+
"throughput_samples_per_sec": 116666.59
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/massive/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.99 0.99 0.99 957
|
| 5 |
+
de 1.00 1.00 1.00 1142
|
| 6 |
+
el 1.00 1.00 1.00 1141
|
| 7 |
+
en 0.99 1.00 1.00 963
|
| 8 |
+
es 0.99 0.99 0.99 1097
|
| 9 |
+
fi 1.00 1.00 1.00 1044
|
| 10 |
+
fr 1.00 1.00 1.00 1221
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 1.00 1.00 1.00 1093
|
| 13 |
+
ja 1.00 1.00 1.00 1337
|
| 14 |
+
ko 1.00 1.00 1.00 1324
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 1.00 1.00 1.00 1129
|
| 19 |
+
pl 1.00 1.00 1.00 1002
|
| 20 |
+
pt 0.99 0.99 0.99 1070
|
| 21 |
+
ro 1.00 1.00 1.00 1010
|
| 22 |
+
ru 1.00 1.00 1.00 1023
|
| 23 |
+
sl 1.00 1.00 1.00 998
|
| 24 |
+
sv 1.00 0.99 1.00 957
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 1.00 1.00 1.00 874
|
| 27 |
+
|
| 28 |
+
accuracy 1.00 19382
|
| 29 |
+
macro avg 0.75 0.75 0.75 19382
|
| 30 |
+
weighted avg 1.00 1.00 1.00 19382
|
fasttext/massive/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.977225,1.74
|
| 3 |
+
5,0.1,0.991856,1.75
|
| 4 |
+
5,0.5,0.996541,1.75
|
| 5 |
+
10,0.05,0.99133,2.95
|
| 6 |
+
10,0.1,0.995319,2.75
|
| 7 |
+
10,0.5,0.997328,2.85
|
| 8 |
+
25,0.05,0.9957,6.56
|
| 9 |
+
25,0.1,0.99683,6.66
|
| 10 |
+
25,0.5,0.997896,6.36
|
fasttext/massive/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "massive"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9974202868641007,
|
| 17 |
+
"f1_macro": 0.9973576195821323,
|
| 18 |
+
"precision_macro": 0.9973036655049542,
|
| 19 |
+
"recall_macro": 0.997414755322694,
|
| 20 |
+
"inference_time_total_s": 0.3027,
|
| 21 |
+
"inference_time_ms_per_sample": 0.015618,
|
| 22 |
+
"throughput_samples_per_sec": 64029.03
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/massive/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.99 0.99 0.99 957
|
| 5 |
+
de 1.00 1.00 1.00 1142
|
| 6 |
+
el 1.00 1.00 1.00 1141
|
| 7 |
+
en 0.99 1.00 0.99 963
|
| 8 |
+
es 0.99 0.99 0.99 1097
|
| 9 |
+
fi 1.00 0.97 0.98 1044
|
| 10 |
+
fr 1.00 1.00 1.00 1221
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 1.00 0.99 0.99 1093
|
| 13 |
+
ja 0.58 0.98 0.73 1337
|
| 14 |
+
ko 1.00 0.98 0.99 1324
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 1.00 0.99 1.00 1129
|
| 19 |
+
pl 1.00 1.00 1.00 1002
|
| 20 |
+
pt 0.99 0.99 0.99 1070
|
| 21 |
+
ro 1.00 1.00 1.00 1010
|
| 22 |
+
ru 1.00 0.99 0.99 1023
|
| 23 |
+
sl 1.00 0.99 0.99 998
|
| 24 |
+
sv 0.99 0.99 0.99 957
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 0.25 0.02 0.03 874
|
| 27 |
+
|
| 28 |
+
accuracy 0.95 19382
|
| 29 |
+
macro avg 0.70 0.70 0.69 19382
|
| 30 |
+
weighted avg 0.93 0.95 0.93 19382
|
fasttext/massive/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.922506,1.17
|
| 3 |
+
5,0.1,0.925959,1.19
|
| 4 |
+
5,0.5,0.927383,1.19
|
| 5 |
+
10,0.05,0.926964,2.29
|
| 6 |
+
10,0.1,0.92742,2.29
|
| 7 |
+
10,0.5,0.929578,2.29
|
| 8 |
+
25,0.05,0.928807,5.7
|
| 9 |
+
25,0.1,0.928194,5.7
|
| 10 |
+
25,0.5,0.92868,5.6
|
fasttext/massive/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "massive"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "10",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9462903725105768,
|
| 17 |
+
"f1_macro": 0.925781433897811,
|
| 18 |
+
"precision_macro": 0.9319867882832494,
|
| 19 |
+
"recall_macro": 0.9363424072842795,
|
| 20 |
+
"inference_time_total_s": 0.1498,
|
| 21 |
+
"inference_time_ms_per_sample": 0.007729,
|
| 22 |
+
"throughput_samples_per_sec": 129385.1
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/mmarco/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.00 0.00 0.00 0
|
| 5 |
+
de 1.00 1.00 1.00 20033
|
| 6 |
+
el 0.00 0.00 0.00 0
|
| 7 |
+
en 1.00 1.00 1.00 20044
|
| 8 |
+
es 0.99 1.00 1.00 20016
|
| 9 |
+
fi 0.00 0.00 0.00 0
|
| 10 |
+
fr 1.00 1.00 1.00 19956
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 1.00 1.00 1.00 19992
|
| 13 |
+
ja 1.00 1.00 1.00 19935
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 1.00 1.00 1.00 19942
|
| 19 |
+
pl 0.00 0.00 0.00 0
|
| 20 |
+
pt 0.99 1.00 0.99 20036
|
| 21 |
+
ro 0.00 0.00 0.00 0
|
| 22 |
+
ru 1.00 1.00 1.00 20086
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 0.00 0.00 0.00 0
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 1.00 1.00 1.00 20005
|
| 27 |
+
|
| 28 |
+
accuracy 1.00 200045
|
| 29 |
+
macro avg 0.42 0.42 0.42 200045
|
| 30 |
+
weighted avg 1.00 1.00 1.00 200045
|
fasttext/mmarco/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.996202,12.13
|
| 3 |
+
5,0.1,0.996976,11.92
|
| 4 |
+
5,0.5,0.997588,12.07
|
| 5 |
+
10,0.05,0.997001,22.21
|
| 6 |
+
10,0.1,0.997388,22.1
|
| 7 |
+
10,0.5,0.997613,22.43
|
| 8 |
+
25,0.05,0.997401,51.92
|
| 9 |
+
25,0.1,0.997526,51.68
|
| 10 |
+
25,0.5,0.997551,51.37
|
fasttext/mmarco/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "mmarco"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "10",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9974555724961883,
|
| 17 |
+
"f1_macro": 0.997456340339345,
|
| 18 |
+
"precision_macro": 0.9974583357050643,
|
| 19 |
+
"recall_macro": 0.9974550318130501,
|
| 20 |
+
"inference_time_total_s": 2.8056,
|
| 21 |
+
"inference_time_ms_per_sample": 0.014025,
|
| 22 |
+
"throughput_samples_per_sec": 71302.41
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/mmarco/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.00 0.00 0.00 0
|
| 5 |
+
de 1.00 0.98 0.99 20033
|
| 6 |
+
el 0.00 0.00 0.00 0
|
| 7 |
+
en 0.99 1.00 0.99 20044
|
| 8 |
+
es 0.99 0.99 0.99 20016
|
| 9 |
+
fi 0.00 0.00 0.00 0
|
| 10 |
+
fr 1.00 0.99 1.00 19956
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 1.00 0.99 1.00 19992
|
| 13 |
+
ja 0.54 1.00 0.70 19935
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 1.00 0.99 0.99 19942
|
| 19 |
+
pl 0.00 0.00 0.00 0
|
| 20 |
+
pt 0.99 0.99 0.99 20036
|
| 21 |
+
ro 0.00 0.00 0.00 0
|
| 22 |
+
ru 1.00 0.99 1.00 20086
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 0.00 0.00 0.00 0
|
| 25 |
+
uk 0.00 0.00 0.00 0
|
| 26 |
+
zh 0.92 0.18 0.30 20005
|
| 27 |
+
|
| 28 |
+
accuracy 0.91 200045
|
| 29 |
+
macro avg 0.39 0.38 0.37 200045
|
| 30 |
+
weighted avg 0.94 0.91 0.89 200045
|
fasttext/mmarco/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.89358,8.71
|
| 3 |
+
5,0.1,0.894014,8.62
|
| 4 |
+
5,0.5,0.894589,8.84
|
| 5 |
+
10,0.05,0.894002,15.85
|
| 6 |
+
10,0.1,0.894355,17.07
|
| 7 |
+
10,0.5,0.894588,16.88
|
| 8 |
+
25,0.05,0.894251,39.96
|
| 9 |
+
25,0.1,0.894636,39.23
|
| 10 |
+
25,0.5,0.894333,44.92
|
fasttext/mmarco/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "mmarco"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.1"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9102751880826814,
|
| 17 |
+
"f1_macro": 0.8945281297744344,
|
| 18 |
+
"precision_macro": 0.9426291281301638,
|
| 19 |
+
"recall_macro": 0.9102787400266008,
|
| 20 |
+
"inference_time_total_s": 1.3177,
|
| 21 |
+
"inference_time_ms_per_sample": 0.006587,
|
| 22 |
+
"throughput_samples_per_sec": 151808.34
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/multilingual_cc_news/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 1.00 1.00 1.00 20015
|
| 4 |
+
da 0.99 0.99 0.99 20013
|
| 5 |
+
de 1.00 1.00 1.00 20008
|
| 6 |
+
el 1.00 1.00 1.00 20050
|
| 7 |
+
en 0.99 1.00 0.99 20005
|
| 8 |
+
es 0.00 0.00 0.00 0
|
| 9 |
+
fi 1.00 1.00 1.00 20084
|
| 10 |
+
fr 0.00 0.00 0.00 0
|
| 11 |
+
hr 1.00 1.00 1.00 19914
|
| 12 |
+
it 0.00 0.00 0.00 0
|
| 13 |
+
ja 1.00 1.00 1.00 20022
|
| 14 |
+
ko 1.00 1.00 1.00 20000
|
| 15 |
+
lt 1.00 0.99 1.00 4950
|
| 16 |
+
mk 1.00 1.00 1.00 18680
|
| 17 |
+
nb 0.99 0.99 0.99 19959
|
| 18 |
+
nl 1.00 1.00 1.00 20009
|
| 19 |
+
pl 1.00 1.00 1.00 20033
|
| 20 |
+
pt 1.00 1.00 1.00 19949
|
| 21 |
+
ro 1.00 1.00 1.00 20113
|
| 22 |
+
ru 1.00 1.00 1.00 20065
|
| 23 |
+
sl 1.00 1.00 1.00 19951
|
| 24 |
+
sv 1.00 1.00 1.00 20044
|
| 25 |
+
uk 1.00 1.00 1.00 20033
|
| 26 |
+
zh 1.00 1.00 1.00 19979
|
| 27 |
+
|
| 28 |
+
accuracy 1.00 403876
|
| 29 |
+
macro avg 0.87 0.87 0.87 403876
|
| 30 |
+
weighted avg 1.00 1.00 1.00 403876
|
fasttext/multilingual_cc_news/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.99585,52.53
|
| 3 |
+
5,0.1,0.99649,52.76
|
| 4 |
+
5,0.5,0.996956,52.74
|
| 5 |
+
10,0.05,0.996493,94.23
|
| 6 |
+
10,0.1,0.996703,93.75
|
| 7 |
+
10,0.5,0.996912,94.22
|
| 8 |
+
25,0.05,0.996813,214.21
|
| 9 |
+
25,0.1,0.996912,212.85
|
| 10 |
+
25,0.5,0.996966,213.97
|
fasttext/multilingual_cc_news/fasttext_subword/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_subword",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "subword",
|
| 5 |
+
"wordNgrams": "2",
|
| 6 |
+
"minn": "2",
|
| 7 |
+
"maxn": "4",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "multilingual_cc_news"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "25",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9970882151947628,
|
| 17 |
+
"f1_macro": 0.9970508016807916,
|
| 18 |
+
"precision_macro": 0.9971120291073209,
|
| 19 |
+
"recall_macro": 0.9969945619392637,
|
| 20 |
+
"inference_time_total_s": 14.7611,
|
| 21 |
+
"inference_time_ms_per_sample": 0.036548,
|
| 22 |
+
"throughput_samples_per_sec": 27360.9
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/multilingual_cc_news/fasttext_word/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
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|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 1.00 1.00 1.00 20015
|
| 4 |
+
da 0.99 0.98 0.99 20013
|
| 5 |
+
de 1.00 1.00 1.00 20008
|
| 6 |
+
el 1.00 1.00 1.00 20050
|
| 7 |
+
en 0.98 1.00 0.99 20005
|
| 8 |
+
es 0.00 0.00 0.00 0
|
| 9 |
+
fi 1.00 0.99 1.00 20084
|
| 10 |
+
fr 0.00 0.00 0.00 0
|
| 11 |
+
hr 1.00 1.00 1.00 19914
|
| 12 |
+
it 0.00 0.00 0.00 0
|
| 13 |
+
ja 0.82 0.98 0.89 20022
|
| 14 |
+
ko 1.00 0.98 0.99 20000
|
| 15 |
+
lt 1.00 0.99 0.99 4950
|
| 16 |
+
mk 1.00 1.00 1.00 18680
|
| 17 |
+
nb 0.98 0.99 0.99 19959
|
| 18 |
+
nl 1.00 1.00 1.00 20009
|
| 19 |
+
pl 1.00 1.00 1.00 20033
|
| 20 |
+
pt 1.00 1.00 1.00 19949
|
| 21 |
+
ro 1.00 1.00 1.00 20113
|
| 22 |
+
ru 1.00 1.00 1.00 20065
|
| 23 |
+
sl 1.00 1.00 1.00 19951
|
| 24 |
+
sv 1.00 1.00 1.00 20044
|
| 25 |
+
uk 1.00 0.99 1.00 20033
|
| 26 |
+
zh 0.98 0.80 0.88 19979
|
| 27 |
+
|
| 28 |
+
accuracy 0.98 403876
|
| 29 |
+
macro avg 0.86 0.86 0.86 403876
|
| 30 |
+
weighted avg 0.99 0.98 0.98 403876
|
fasttext/multilingual_cc_news/fasttext_word/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.984308,31.82
|
| 3 |
+
5,0.1,0.984619,32.82
|
| 4 |
+
5,0.5,0.985011,32.56
|
| 5 |
+
10,0.05,0.984564,56.96
|
| 6 |
+
10,0.1,0.984768,59.4
|
| 7 |
+
10,0.5,0.98506,57.74
|
| 8 |
+
25,0.05,0.984702,131.39
|
| 9 |
+
25,0.1,0.984856,132.89
|
| 10 |
+
25,0.5,0.985055,133.91
|
fasttext/multilingual_cc_news/fasttext_word/run_metadata.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"run_name": "fasttext_word",
|
| 3 |
+
"tags": {
|
| 4 |
+
"feature_type": "word_ngram",
|
| 5 |
+
"wordNgrams": "1",
|
| 6 |
+
"minn": "0",
|
| 7 |
+
"maxn": "0",
|
| 8 |
+
"loss": "softmax",
|
| 9 |
+
"dataset_version": "multilingual_cc_news"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"epoch": "10",
|
| 13 |
+
"lr": "0.5"
|
| 14 |
+
},
|
| 15 |
+
"metrics": {
|
| 16 |
+
"accuracy": 0.9847775059671781,
|
| 17 |
+
"f1_macro": 0.985102850565851,
|
| 18 |
+
"precision_macro": 0.9868653316205467,
|
| 19 |
+
"recall_macro": 0.9849375987593945,
|
| 20 |
+
"inference_time_total_s": 5.1684,
|
| 21 |
+
"inference_time_ms_per_sample": 0.012797,
|
| 22 |
+
"throughput_samples_per_sec": 78142.71
|
| 23 |
+
}
|
| 24 |
+
}
|
fasttext/multilingual_toxicity_dataset/fasttext_subword/classification_report.txt
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
ca 0.00 0.00 0.00 0
|
| 4 |
+
da 0.00 0.00 0.00 0
|
| 5 |
+
de 1.00 1.00 1.00 506
|
| 6 |
+
el 0.00 0.00 0.00 0
|
| 7 |
+
en 0.99 1.00 0.99 478
|
| 8 |
+
es 1.00 0.99 0.99 495
|
| 9 |
+
fi 0.00 0.00 0.00 0
|
| 10 |
+
fr 1.00 0.99 0.99 472
|
| 11 |
+
hr 0.00 0.00 0.00 0
|
| 12 |
+
it 0.99 1.00 0.99 478
|
| 13 |
+
ja 1.00 0.99 0.99 493
|
| 14 |
+
ko 0.00 0.00 0.00 0
|
| 15 |
+
lt 0.00 0.00 0.00 0
|
| 16 |
+
mk 0.00 0.00 0.00 0
|
| 17 |
+
nb 0.00 0.00 0.00 0
|
| 18 |
+
nl 0.00 0.00 0.00 0
|
| 19 |
+
pl 0.00 0.00 0.00 0
|
| 20 |
+
pt 0.00 0.00 0.00 0
|
| 21 |
+
ro 0.00 0.00 0.00 0
|
| 22 |
+
ru 1.00 1.00 1.00 492
|
| 23 |
+
sl 0.00 0.00 0.00 0
|
| 24 |
+
sv 0.00 0.00 0.00 0
|
| 25 |
+
uk 1.00 1.00 1.00 455
|
| 26 |
+
zh 0.98 1.00 0.99 455
|
| 27 |
+
|
| 28 |
+
accuracy 0.99 4324
|
| 29 |
+
macro avg 0.37 0.37 0.37 4324
|
| 30 |
+
weighted avg 0.99 0.99 0.99 4324
|
fasttext/multilingual_toxicity_dataset/fasttext_subword/param_search_results.csv
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,lr,val_f1_macro,train_time_s
|
| 2 |
+
5,0.05,0.196243,0.87
|
| 3 |
+
5,0.1,0.716703,0.87
|
| 4 |
+
5,0.5,0.990663,0.88
|
| 5 |
+
10,0.05,0.722254,1.07
|
| 6 |
+
10,0.1,0.98601,1.08
|
| 7 |
+
10,0.5,0.991235,1.08
|
| 8 |
+
25,0.05,0.987171,1.78
|
| 9 |
+
25,0.1,0.991235,1.88
|
| 10 |
+
25,0.5,0.991823,1.89
|