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deadcode99/mistral-billm-token-classification-segmentwise

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ base_model: mistralai/Mistral-7B-Instruct-v0.2
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: mistral-7b-32k-billm-finetuned-token-classification-segmentwise
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+ results: []
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+ ---
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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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+
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+ # mistral-7b-32k-billm-finetuned-token-classification-segmentwise
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+
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+ This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3392
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+ - Precision: 0.0
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+ - Recall: 0.0
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+ - F1: 0.0
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+ - Accuracy: 0.7072
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 32
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+ - total_train_batch_size: 32
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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: 4
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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+ | No log | 0.9784 | 34 | 1.5589 | 0.0 | 0.0 | 0.0 | 0.6931 |
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+ | No log | 1.9856 | 69 | 1.3685 | 0.0 | 0.0 | 0.0 | 0.7023 |
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+ | No log | 2.9928 | 104 | 1.3357 | 0.0 | 0.0 | 0.0 | 0.7067 |
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+ | No log | 3.9137 | 136 | 1.3392 | 0.0 | 0.0 | 0.0 | 0.7072 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.10.0
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+ - Transformers 4.40.1
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+ - Pytorch 2.2.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.19.1
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