Instructions to use ryanyip7777/intent_bert_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryanyip7777/intent_bert_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ryanyip7777/intent_bert_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ryanyip7777/intent_bert_classifier") model = AutoModelForSequenceClassification.from_pretrained("ryanyip7777/intent_bert_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download intent_classifier.log from ryanyip7777/intent_bert_classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/ryanyip7777/intent_bert_classifier/resolve/main/intent_classifier.log
- Command line
-
hf download hf://ryanyip7777/intent_bert_classifier/intent_classifier.log
-
curl -L -o intent_classifier.log https://huggingface.co/ryanyip7777/intent_bert_classifier/resolve/main/intent_classifier.log
1.84 kB
| Training/evaluation parameters Namespace(data_dir='intent_example/', model_dir='/data/ryanyip/huggingface-models/', model_name='mc_bert_base', output_dir='./intent_test', do_train=True, do_predict=False, result_output_dir='./intent_test/result/', max_length=128, train_batch_size=8, eval_batch_size=8, learning_rate=5e-05, weight_decay=0.01, adam_epsilon=1e-08, max_grad_norm=1.0, epochs=3, warmup_proportion=0.1, earlystop_patience=2, logging_steps=10, save_steps=1000, seed=2021, device=device(type='cuda'), model_type='bert', task_name='qic') | |
| ***** Running training ***** | |
| Num samples 1350 | |
| Num epochs 3 | |
| Num training steps 507 | |
| Num warmup steps 50 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.1 | |
| Saving models checkpoint to ./intent_test/checkpoint-10 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.18823529411764706 | |
| Saving models checkpoint to ./intent_test/checkpoint-20 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.2235294117647059 | |
| Saving models checkpoint to ./intent_test/checkpoint-30 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.24705882352941178 | |
| Saving models checkpoint to ./intent_test/checkpoint-40 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.2529411764705882 | |
| Saving models checkpoint to ./intent_test/checkpoint-50 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.32941176470588235 | |
| Saving models checkpoint to ./intent_test/checkpoint-60 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.2647058823529412 | |
| Earlystopper counter: 1 out of 2 | |
| ***** Running evaluation ***** | |
| Num samples 170 | |
| qic-mc_bert_base acc: 0.27647058823529413 | |
| Earlystopper counter: 2 out of 2 | |
| Training Stop! The best step 60: 0.32941176470588235 | |
| Saving models checkpoint to ./intent_test | |