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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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library_name: span-marker |
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tags: |
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- token-classification |
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- named-enity-recognition |
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datasets: |
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- DFKI-SLT/few-nerd |
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pipeline_tag: token-classification |
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base_model: roberta-base |
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model-index: |
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- name: span-marker-robert-base |
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results: [] |
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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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# span-marker-robert-base |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on [few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset using [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) an module for NER. |
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# Usage |
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```python |
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from span_marker import SpanMarkerModel |
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model = SpanMarkerModel.from_pretrained("krinal/span-marker-robert-base") |
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ner_result = model.predict("Argentine captain Lionel Messi won Golden Ball at FIFA world cup 2022") |
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``` |
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## Training and evaluation data |
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### Training hyperparameters |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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_ratio: 0.1 |
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- num_epochs: 1 |
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### Evaluation |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0214 |
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- Overall Precision: 0.7642 |
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- Overall Recall: 0.7947 |
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- Overall F1: 0.7791 |
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- Overall Accuracy: 0.9397 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0214 | 0.08 | 100 | 0.0219 | 0.7641 | 0.7679 | 0.7660 | 0.9330 | |
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| 0.0199 | 0.16 | 200 | 0.0243 | 0.7442 | 0.7679 | 0.7559 | 0.9348 | |
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| 0.0179 | 0.24 | 300 | 0.0212 | 0.7730 | 0.7580 | 0.7654 | 0.9361 | |
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| 0.0188 | 0.33 | 400 | 0.0225 | 0.7616 | 0.7710 | 0.7662 | 0.9343 | |
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| 0.0149 | 0.41 | 500 | 0.0240 | 0.7537 | 0.7783 | 0.7658 | 0.9375 | |
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| 0.015 | 0.49 | 600 | 0.0230 | 0.7540 | 0.7829 | 0.7682 | 0.9362 | |
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| 0.0137 | 0.57 | 700 | 0.0232 | 0.7746 | 0.7538 | 0.7640 | 0.9319 | |
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| 0.0123 | 0.65 | 800 | 0.0218 | 0.7651 | 0.7879 | 0.7763 | 0.9393 | |
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| 0.0103 | 0.73 | 900 | 0.0223 | 0.7688 | 0.7964 | 0.7824 | 0.9397 | |
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| 0.0108 | 0.82 | 1000 | 0.0209 | 0.7763 | 0.7816 | 0.7789 | 0.9397 | |
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| 0.0116 | 0.9 | 1100 | 0.0213 | 0.7743 | 0.7879 | 0.7811 | 0.9398 | |
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| 0.0119 | 0.98 | 1200 | 0.0214 | 0.7653 | 0.7947 | 0.7797 | 0.9400 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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- span-marker 1.2.3 |
