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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}
```
"""
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