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--- |
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language: |
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- en |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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tags: |
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- pytorch |
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- BertForTokenClassification |
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- named-entity-recognition |
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- roberta-base |
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- generated_from_trainer |
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metrics: |
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- recall |
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- precision |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-tiny-ontonotes |
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results: |
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- task: |
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type: token-classification |
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metrics: |
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- type: accuracy |
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value: 0.9476 |
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name: accuracy |
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- type: precision |
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value: 0.6817 |
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name: precision |
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- type: accuracy |
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value: 0.7193 |
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name: recall |
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- type: accuracy |
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value: 0.7 |
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name: F1 |
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datasets: |
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- tner/ontonotes5 |
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library_name: transformers |
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pipeline_tag: token-classification |
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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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# bert-tiny-ontonotes |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1917 |
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- Recall: 0.7193 |
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- Precision: 0.6817 |
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- F1: 0.7000 |
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- Accuracy: 0.9476 |
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## How to use the Model |
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### Using pipeline |
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```python |
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from transformers import pipeline |
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import torch |
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# Initiate the pipeline |
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device = 0 if torch.cuda.is_available() else 'cpu' |
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ner = pipeline("token-classification", "arnabdhar/bert-tiny-ontonotes", device=device) |
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# use the pipeline |
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input_text = "My name is Clara and I live in Berkeley, California." |
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results = ner(input_text) |
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``` |
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## Intended uses & limitations |
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This model is fine-tuned for **Named Entity Recognition** task and you can use the model as it is or can use this model as a base model for further fine tuning on your custom dataset. |
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The following entities were fine-tuned on: |
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CARDINAL, DATE, PERSON, NORP, GPE, LAW, PERCENT, ORDINAL, MONEY, WORK_OF_ART, FAC, TIME, QUANTITY, PRODUCT, LANGUAGE, ORG, LOC, EVENT |
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## Training and evaluation data |
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The dataset has 3 partitions, `train`, `validation` and `test`, all the 3 partitions were combined and then a 80:20 train-test split was made for finet uning process. The following `ID2LABEL` mapping was used. |
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```json |
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{ |
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"0": "O", |
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"1": "B-CARDINAL", |
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"2": "B-DATE", |
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"3": "I-DATE", |
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"4": "B-PERSON", |
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"5": "I-PERSON", |
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"6": "B-NORP", |
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"7": "B-GPE", |
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"8": "I-GPE", |
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"9": "B-LAW", |
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"10": "I-LAW", |
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"11": "B-ORG", |
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"12": "I-ORG", |
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"13": "B-PERCENT", |
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"14": "I-PERCENT", |
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"15": "B-ORDINAL", |
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"16": "B-MONEY", |
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"17": "I-MONEY", |
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"18": "B-WORK_OF_ART", |
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"19": "I-WORK_OF_ART", |
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"20": "B-FAC", |
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"21": "B-TIME", |
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"22": "I-CARDINAL", |
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"23": "B-LOC", |
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"24": "B-QUANTITY", |
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"25": "I-QUANTITY", |
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"26": "I-NORP", |
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"27": "I-LOC", |
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"28": "B-PRODUCT", |
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"29": "I-TIME", |
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"30": "B-EVENT", |
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"31": "I-EVENT", |
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"32": "I-FAC", |
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"33": "B-LANGUAGE", |
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"34": "I-PRODUCT", |
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"35": "I-ORDINAL", |
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"36": "I-LANGUAGE" |
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} |
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``` |
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## Training procedure |
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The model was finetuned on Google Colab with a __NVIDIA T4__ GPU with 15GB of VRAM. It took around 5 minutes to fine tune and evaluate the model with 6000 steps of total training steps. For more details, you can look into the [Weights & Biases](https://wandb.ai/2wb2ndur/NER-ontonotes/runs/d93imv8j/overview?workspace=user-2wb2ndur) log history. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 160 |
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- seed: 75241309 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 6000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 0.4283 | 0.31 | 600 | 0.3864 | 0.4561 | 0.4260 | 0.4405 | 0.9058 | |
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| 0.3214 | 0.63 | 1200 | 0.2865 | 0.5865 | 0.5485 | 0.5669 | 0.9265 | |
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| 0.2886 | 0.94 | 1800 | 0.2439 | 0.6432 | 0.6165 | 0.6295 | 0.9354 | |
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| 0.2511 | 1.25 | 2400 | 0.2233 | 0.6765 | 0.6250 | 0.6497 | 0.9389 | |
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| 0.2224 | 1.56 | 3000 | 0.2088 | 0.6878 | 0.6642 | 0.6758 | 0.9433 | |
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| 0.2181 | 1.88 | 3600 | 0.2001 | 0.7105 | 0.6684 | 0.6888 | 0.9451 | |
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| 0.215 | 2.19 | 4200 | 0.1954 | 0.7140 | 0.6795 | 0.6963 | 0.9469 | |
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| 0.1907 | 2.5 | 4800 | 0.1934 | 0.7169 | 0.6776 | 0.6967 | 0.9470 | |
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| 0.209 | 2.82 | 5400 | 0.1918 | 0.7185 | 0.6812 | 0.6994 | 0.9475 | |
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| 0.2073 | 3.13 | 6000 | 0.1917 | 0.7193 | 0.6817 | 0.7000 | 0.9476 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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