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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/roberta-large |
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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: cohere_generated_abstracts_roberta |
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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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# cohere_generated_abstracts_roberta |
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Accuracy: 1.0 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.0078 | 0.0838 | 100 | 0.0029 | 0.9996 | |
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| 0.0036 | 0.1676 | 200 | 0.0053 | 0.9992 | |
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| 0.0064 | 0.2515 | 300 | 0.0012 | 0.9999 | |
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| 0.002 | 0.3353 | 400 | 0.0028 | 0.9996 | |
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| 0.0019 | 0.4191 | 500 | 0.0009 | 0.9999 | |
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| 0.0014 | 0.5029 | 600 | 0.0026 | 0.9998 | |
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| 0.0003 | 0.5868 | 700 | 0.0012 | 0.9999 | |
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| 0.0017 | 0.6706 | 800 | 0.0000 | 1.0 | |
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| 0.0015 | 0.7544 | 900 | 0.0000 | 1.0 | |
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| 0.0019 | 0.8382 | 1000 | 0.0007 | 0.9999 | |
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| 0.0033 | 0.9220 | 1100 | 0.0048 | 0.9994 | |
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| 0.0013 | 1.0059 | 1200 | 0.0001 | 1.0 | |
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| 0.0032 | 1.0897 | 1300 | 0.0015 | 0.9998 | |
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| 0.0013 | 1.1735 | 1400 | 0.0000 | 1.0 | |
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| 0.0 | 1.2573 | 1500 | 0.0000 | 1.0 | |
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| 0.0 | 1.3412 | 1600 | 0.0000 | 1.0 | |
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| 0.0 | 1.4250 | 1700 | 0.0000 | 1.0 | |
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| 0.0003 | 1.5088 | 1800 | 0.0023 | 0.9996 | |
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| 0.0005 | 1.5926 | 1900 | 0.0000 | 1.0 | |
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| 0.0 | 1.6764 | 2000 | 0.0000 | 1.0 | |
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| 0.0 | 1.7603 | 2100 | 0.0000 | 1.0 | |
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| 0.0 | 1.8441 | 2200 | 0.0000 | 1.0 | |
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| 0.0 | 1.9279 | 2300 | 0.0000 | 1.0 | |
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| 0.0 | 2.0117 | 2400 | 0.0000 | 1.0 | |
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| 0.0 | 2.0956 | 2500 | 0.0000 | 1.0 | |
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| 0.0 | 2.1794 | 2600 | 0.0000 | 1.0 | |
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| 0.0 | 2.2632 | 2700 | 0.0000 | 1.0 | |
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| 0.0 | 2.3470 | 2800 | 0.0000 | 1.0 | |
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| 0.0 | 2.4308 | 2900 | 0.0000 | 1.0 | |
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| 0.0 | 2.5147 | 3000 | 0.0000 | 1.0 | |
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| 0.0 | 2.5985 | 3100 | 0.0000 | 1.0 | |
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| 0.0 | 2.6823 | 3200 | 0.0000 | 1.0 | |
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| 0.0 | 2.7661 | 3300 | 0.0000 | 1.0 | |
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| 0.0 | 2.8500 | 3400 | 0.0000 | 1.0 | |
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| 0.0 | 2.9338 | 3500 | 0.0000 | 1.0 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.5.0+cu124 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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