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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert/distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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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: distilbert-NER
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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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+ # distilbert-NER
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+
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+ This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0649
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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: 0.9838
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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: 64
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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: 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 | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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+ | No log | 0.91 | 200 | 0.0681 | 0.0 | 0.0 | 0.0 | 0.9805 |
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+ | No log | 1.82 | 400 | 0.0599 | 0.0 | 0.0 | 0.0 | 0.9827 |
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+ | 0.1171 | 2.73 | 600 | 0.0641 | 0.0 | 0.0 | 0.0 | 0.9834 |
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+ | 0.1171 | 3.64 | 800 | 0.0652 | 0.0 | 0.0 | 0.0 | 0.9843 |
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+ | 0.0177 | 4.55 | 1000 | 0.0649 | 0.0 | 0.0 | 0.0 | 0.9838 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.1
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
config.json ADDED
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+ {
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+ "_name_or_path": "distilbert/distilbert-base-uncased",
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+ "activation": "gelu",
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+ "architectures": [
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+ "DistilBertForTokenClassification"
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+ ],
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+ "attention_dropout": 0.1,
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+ "dim": 768,
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+ "dropout": 0.1,
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+ "hidden_dim": 3072,
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+ "id2label": {
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+ "max_position_embeddings": 512,
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+ "model_type": "distilbert",
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+ "n_layers": 6,
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+ "sinusoidal_pos_embds": false,
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+ "tie_weights_": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.1",
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+ "vocab_size": 30522
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+ }
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