Instructions to use bambadij/sentiment_analysis_model_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bambadij/sentiment_analysis_model_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bambadij/sentiment_analysis_model_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bambadij/sentiment_analysis_model_trainer") model = AutoModelForSequenceClassification.from_pretrained("bambadij/sentiment_analysis_model_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bambadij/sentiment_analysis_model_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
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https://huggingface.co/bambadij/sentiment_analysis_model_trainer/resolve/main/README.md
- Command line
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hf download hf://bambadij/sentiment_analysis_model_trainer/README.md
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curl -L -o README.md https://huggingface.co/bambadij/sentiment_analysis_model_trainer/resolve/main/README.md
1.51 kB
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: sentiment_analysis_model_trainer | |
| 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. --> | |
| # sentiment_analysis_model_trainer | |
| 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: 0.6184 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.6926 | 1.0 | 1000 | 0.6214 | | |
| | 0.5621 | 2.0 | 2000 | 0.6184 | | |
| | 0.398 | 3.0 | 3000 | 0.7893 | | |
| | 0.2447 | 4.0 | 4000 | 1.1513 | | |
| | 0.1501 | 5.0 | 5000 | 1.3035 | | |
| ### Framework versions | |
| - Transformers 4.35.0 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |