Instructions to use Jedida/sentence_sentiments_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/sentence_sentiments_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/sentence_sentiments_analysis_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/sentence_sentiments_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("Jedida/sentence_sentiments_analysis_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jedida/sentence_sentiments_analysis_bert: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
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https://huggingface.co/Jedida/sentence_sentiments_analysis_bert/resolve/main/README.md
- Command line
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hf download hf://Jedida/sentence_sentiments_analysis_bert/README.md
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curl -L -o README.md https://huggingface.co/Jedida/sentence_sentiments_analysis_bert/resolve/main/README.md
1.62 kB
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: sentence_sentiments_analysis_bert | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # sentence_sentiments_analysis_bert | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2690 | |
| - F1-score: 0.9132 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1-score | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| | |
| | 0.3365 | 1.0 | 2500 | 0.3218 | 0.9066 | | |
| | 0.2477 | 2.0 | 5000 | 0.2690 | 0.9132 | | |
| | 0.1417 | 3.0 | 7500 | 0.3876 | 0.9178 | | |
| | 0.0645 | 4.0 | 10000 | 0.4436 | 0.9216 | | |
| | 0.0304 | 5.0 | 12500 | 0.5194 | 0.9208 | | |
| ### Framework versions | |
| - Transformers 4.33.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |