End of training
Browse files- README.md +64 -0
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: answerdotai/ModernBERT-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: modernbert-emotion
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# modernbert-emotion
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2534
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- Accuracy: 0.932
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- F1 Macro: 0.8871
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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| 0.2147 | 1.0 | 2000 | 0.1959 | 0.9325 | 0.9062 |
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| 0.1252 | 2.0 | 4000 | 0.1597 | 0.934 | 0.9034 |
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| 0.0573 | 3.0 | 6000 | 0.2126 | 0.939 | 0.9107 |
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### Framework versions
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- Transformers 5.3.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.8.3
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- Tokenizers 0.22.2
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"is_local": false,
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 8192,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "[UNK]"
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}
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