| import os | |
| from typing import Dict | |
| def get_readme(model_name: str, | |
| metric: Dict, | |
| metric_span: Dict, | |
| config: Dict): | |
| language_model = config['model'] | |
| dataset = None | |
| dataset_alias = "custom" | |
| if config["dataset"] is not None: | |
| dataset = sorted([i for i in config["dataset"]]) | |
| dataset_alias = ','.join(dataset) | |
| config_text = "\n".join([f" - {k}: {v}" for k, v in config.items()]) | |
| ci_micro = '\n'.join([f' - {k}%: {v}' for k, v in metric["micro/f1_ci"].items()]) | |
| ci_macro = '\n'.join([f' - {k}%: {v}' for k, v in metric["micro/f1_ci"].items()]) | |
| per_entity_metric = '\n'.join([f'- {k}: {v["f1"]}' for k, v in metric['per_entity_metric'].items()]) | |
| if dataset is None: | |
| dataset_link = 'custom' | |
| else: | |
| dataset = [dataset] if type(dataset) is str else dataset | |
| dataset_link = ','.join([f"[{d}](https://huggingface.co/datasets/{d})" for d in dataset]) | |
| return f"""--- | |
| datasets: | |
| - {dataset_alias} | |
| metrics: | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: {model_name} | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: {dataset_alias} | |
| type: {dataset_alias} | |
| args: {dataset_alias} | |
| metrics: | |
| - name: F1 | |
| type: f1 | |
| value: {metric['micro/f1']} | |
| - name: Precision | |
| type: precision | |
| value: {metric['micro/precision']} | |
| - name: Recall | |
| type: recall | |
| value: {metric['micro/recall']} | |
| - name: F1 (macro) | |
| type: f1_macro | |
| value: {metric['macro/f1']} | |
| - name: Precision (macro) | |
| type: precision_macro | |
| value: {metric['macro/precision']} | |
| - name: Recall (macro) | |
| type: recall_macro | |
| value: {metric['macro/recall']} | |
| - name: F1 (entity span) | |
| type: f1_entity_span | |
| value: {metric_span['micro/f1']} | |
| - name: Precision (entity span) | |
| type: precision_entity_span | |
| value: {metric_span['micro/precision']} | |
| - name: Recall (entity span) | |
| type: recall_entity_span | |
| value: {metric_span['micro/recall']} | |
| pipeline_tag: token-classification | |
| widget: | |
| - text: "Jacob Collier is a Grammy awarded artist from England." | |
| example_title: "NER Example 1" | |
| --- | |
| # {model_name} | |
| This model is a fine-tuned version of [{language_model}](https://huggingface.co/{language_model}) on the | |
| {dataset_link} dataset. | |
| Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository | |
| for more detail). It achieves the following results on the test set: | |
| - F1 (micro): {metric['micro/f1']} | |
| - Precision (micro): {metric['micro/precision']} | |
| - Recall (micro): {metric['micro/recall']} | |
| - F1 (macro): {metric['macro/f1']} | |
| - Precision (macro): {metric['macro/precision']} | |
| - Recall (macro): {metric['macro/recall']} | |
| The per-entity breakdown of the F1 score on the test set are below: | |
| {per_entity_metric} | |
| For F1 scores, the confidence interval is obtained by bootstrap as below: | |
| - F1 (micro): | |
| {ci_micro} | |
| - F1 (macro): | |
| {ci_macro} | |
| Full evaluation can be found at [metric file of NER](https://huggingface.co/{model_name}/raw/main/eval/metric.json) | |
| and [metric file of entity span](https://huggingface.co/{model_name}/raw/main/eval/metric_span.json). | |
| ### Usage | |
| This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip | |
| ```shell | |
| pip install tner | |
| ``` | |
| and activate model as below. | |
| ```python | |
| from tner import TransformersNER | |
| model = TransformersNER("{model_name}") | |
| model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) | |
| ``` | |
| It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| {config_text} | |
| The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/{model_name}/raw/main/trainer_config.json). | |
| ### Reference | |
| If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). | |
| ``` | |
| {bib} | |
| ``` | |
| """ | |