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End of training

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README.md ADDED
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+ ---
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+ license: cc-by-sa-4.0
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+ base_model: nlpaueb/legal-bert-base-uncased
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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: legal-bert-base-uncased-5-epochs-fine-tune
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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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+ # legal-bert-base-uncased-5-epochs-fine-tune
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+
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+ This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8581
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+ - Accuracy: 1
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+ - F1 Micro: 1
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+ - F1 Macro: 1
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+ - F1 Weighted: 1
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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: 5e-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: 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: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|
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+ | 1.348 | 1.0 | 643 | 0.6934 | 1 | 1 | 1 | 1 |
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+ | 0.6471 | 2.0 | 1286 | 0.6506 | 1 | 1 | 1 | 1 |
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+ | 0.4944 | 3.0 | 1929 | 0.7049 | 1 | 1 | 1 | 1 |
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+ | 0.2113 | 4.0 | 2572 | 0.8336 | 1 | 1 | 1 | 1 |
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+ | 0.1352 | 5.0 | 3215 | 0.8581 | 1 | 1 | 1 | 1 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.1.0
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+ - Tokenizers 0.15.2
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+ {
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+ "_name_or_path": "nlpaueb/legal-bert-base-uncased",
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+ "architectures": [
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+ ],
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+ "hidden_act": "gelu",
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "bert",
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ }
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