Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use bdanko/bert-tweeteval-distilroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdanko/bert-tweeteval-distilroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdanko/bert-tweeteval-distilroberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdanko/bert-tweeteval-distilroberta") model = AutoModelForSequenceClassification.from_pretrained("bdanko/bert-tweeteval-distilroberta") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +9 -8
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README.md
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size:
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- seed:
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 20
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1791
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 100
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- seed: 15179996
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 20
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.6591 | 1.0 | 204 | 0.6607 |
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| 0.5444 | 2.0 | 408 | 0.7015 |
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| 0.3962 | 3.0 | 612 | 0.6378 |
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| 0.2703 | 4.0 | 816 | 0.9126 |
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| 0.1748 | 5.0 | 1020 | 1.0216 |
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| 0.1327 | 6.0 | 1224 | 1.1791 |
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### Framework versions
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model.safetensors
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