Update readme.py

readme.py

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+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}
+```
+"""