Instructions to use agaresd/encoder_only_deepL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agaresd/encoder_only_deepL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agaresd/encoder_only_deepL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("agaresd/encoder_only_deepL") model = AutoModelForSequenceClassification.from_pretrained("agaresd/encoder_only_deepL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from agaresd/encoder_only_deepL: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/agaresd/encoder_only_deepL/resolve/main/README.md
- Command line
-
hf download hf://agaresd/encoder_only_deepL/README.md
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curl -L -o README.md https://huggingface.co/agaresd/encoder_only_deepL/resolve/main/README.md
1.76 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/bert_uncased_L-2_H-128_A-2 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: encoder_only_deepL | |
| 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. --> | |
| # encoder_only_deepL | |
| This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3886 | |
| - Accuracy: 0.8343 | |
| - F1: 0.8253 | |
| - Precision: 0.8726 | |
| - Recall: 0.7828 | |
| ## 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: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.4535 | 1.0 | 1563 | 0.4241 | 0.8146 | 0.8004 | 0.8666 | 0.7436 | | |
| | 0.3854 | 2.0 | 3126 | 0.3886 | 0.8343 | 0.8253 | 0.8726 | 0.7828 | | |
| ### Framework versions | |
| - Transformers 4.46.3 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |