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--- |
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license: mit |
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base_model: croissantllm/CroissantCool-v0.2 |
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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: llm2vec-croissant-mntp |
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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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# llm2vec-croissant-mntp |
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This model is a fine-tuned version of [croissantllm/CroissantCool-v0.2](https://huggingface.co/croissantllm/CroissantCool-v0.2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8867 |
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- Accuracy: 0.6078 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 3.0 |
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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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| No log | 0.0884 | 100 | 4.7866 | 0.1990 | |
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| No log | 0.1768 | 200 | 4.0496 | 0.3309 | |
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| No log | 0.2653 | 300 | 3.6525 | 0.3779 | |
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| No log | 0.3537 | 400 | 3.2410 | 0.4258 | |
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| 3.9116 | 0.4421 | 500 | 3.6305 | 0.3912 | |
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| 3.9116 | 0.5305 | 600 | 3.1770 | 0.4406 | |
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| 3.9116 | 0.6189 | 700 | 2.4478 | 0.5199 | |
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| 3.9116 | 0.7073 | 800 | 2.2383 | 0.5508 | |
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| 3.9116 | 0.7958 | 900 | 2.1547 | 0.5635 | |
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| 2.4568 | 0.8842 | 1000 | 2.0868 | 0.5759 | |
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| 2.4568 | 0.9726 | 1100 | 2.0399 | 0.5820 | |
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| 2.4568 | 1.0610 | 1200 | 2.0102 | 0.5873 | |
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| 2.4568 | 1.1494 | 1300 | 1.9805 | 0.5897 | |
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| 2.4568 | 1.2378 | 1400 | 1.9590 | 0.5955 | |
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| 1.9305 | 1.3263 | 1500 | 1.9381 | 0.5982 | |
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| 1.9305 | 1.4147 | 1600 | 1.9249 | 0.5995 | |
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| 1.9305 | 1.5031 | 1700 | 1.9223 | 0.6017 | |
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| 1.9305 | 1.5915 | 1800 | 1.9091 | 0.6037 | |
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| 1.9305 | 1.6799 | 1900 | 1.9038 | 0.6042 | |
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| 1.8511 | 1.7683 | 2000 | 1.8982 | 0.6045 | |
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| 1.8511 | 1.8568 | 2100 | 1.8924 | 0.6060 | |
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| 1.8511 | 1.9452 | 2200 | 1.8844 | 0.6072 | |
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| 1.8511 | 2.0336 | 2300 | 1.8873 | 0.6087 | |
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| 1.8511 | 2.1220 | 2400 | 1.8889 | 0.6068 | |
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| 1.8197 | 2.2104 | 2500 | 1.8848 | 0.6080 | |
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| 1.8197 | 2.2989 | 2600 | 1.8736 | 0.6091 | |
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| 1.8197 | 2.3873 | 2700 | 1.8858 | 0.6072 | |
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| 1.8197 | 2.4757 | 2800 | 1.8814 | 0.6088 | |
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| 1.8197 | 2.5641 | 2900 | 1.8649 | 0.6103 | |
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| 1.8116 | 2.6525 | 3000 | 1.8647 | 0.6091 | |
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| 1.8116 | 2.7409 | 3100 | 1.8755 | 0.6101 | |
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| 1.8116 | 2.8294 | 3200 | 1.8755 | 0.6099 | |
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| 1.8116 | 2.9178 | 3300 | 1.8867 | 0.6078 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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