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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mit_restaurant |
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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-base-uncased-ner-mit-restaurant |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: mit_restaurant |
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type: mit_restaurant |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9118988661540467 |
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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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# distilbert-base-uncased-ner-mit-restaurant |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3097 |
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- Precision: 0.7874 |
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- Recall: 0.8104 |
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- F1: 0.7988 |
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- Accuracy: 0.9119 |
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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: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 431 | 0.4575 | 0.6220 | 0.6856 | 0.6523 | 0.8650 | |
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| 1.1705 | 2.0 | 862 | 0.3183 | 0.7747 | 0.7953 | 0.7848 | 0.9071 | |
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| 0.3254 | 3.0 | 1293 | 0.3163 | 0.7668 | 0.8021 | 0.7841 | 0.9058 | |
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| 0.2287 | 4.0 | 1724 | 0.3097 | 0.7874 | 0.8104 | 0.7988 | 0.9119 | |
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### Framework versions |
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- Transformers 4.8.2 |
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- Pytorch 1.8.1+cu111 |
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- Datasets 1.8.0 |
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- Tokenizers 0.10.3 |
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