Update readme.py

readme.py

@@ -1,10 +1,26 @@
 import os
 from typing import Dict
 
+bib = """
+@inproceedings{dimosthenis-etal-2022-twitter,
+    title = "{T}witter {T}opic {C}lassification",
+    author = "Antypas, Dimosthenis  and
+    Ushio, Asahi  and
+    Camacho-Collados, Jose  and
+    Neves, Leonardo  and
+    Silva, Vitor  and
+    Barbieri, Francesco",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics"
+}
+"""
+
 
 def get_readme(model_name: str,
                metric: Dict,
-               metric_span: Dict,
                config: Dict):
     language_model = config['model']
     dataset = None
@@ -23,100 +39,51 @@ def get_readme(model_name: str,
         dataset_link = ','.join([f"[{d}](https://huggingface.co/datasets/{d})" for d in dataset])
     return f"""---
 datasets:
-- {dataset_alias}
+- cardiffnlp/tweet_topic_multi
 metrics:
 - f1
-- precision
-- recall
+- accuracy
 model-index:
 - name: {model_name}
   results:
   - task:
-      name: Token Classification
-      type: token-classification
+      type: text-classification
+      name: Text Classification
     dataset:
-      name: {dataset_alias}
-      type: {dataset_alias}
-      args: {dataset_alias}
+      name: cardiffnlp/tweet_topic_multi
+      type: cardiffnlp/tweet_topic_multi
+      args: cardiffnlp/tweet_topic_multi
+      split: test_2021 
     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']}
+      value: {metric['test/eval_f1']}
     - 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
+      value: {metric['test/eval_f1_macro']}
+    - name: Accuracy
+      type: accuracy
+      value: {metric['test/eval_accuracy']}
+pipeline_tag: text-classification
 widget:
-- text: "Jacob Collier is a Grammy awarded artist from England."
-  example_title: "NER Example 1"
+- text: "I'm sure the {"{@Tampa Bay Lightning@}"} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
+  example_title: "Example 1"
+- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US." 
+  example_title: "Example 2"
 ---
 # {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']}
+This model is a fine-tuned version of [{language_model}](https://huggingface.co/{language_model}) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
 
-The per-entity breakdown of the F1 score on the test set are below:
-{per_entity_metric} 
+- F1 (micro): {metric['test/eval_f1']}
+- F1 (macro): {metric['test/eval_f1_macro']}
+- Accuracy: {metric['test/eval_accuracy']}
 
-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"])
+pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020", problem_type="multi_label_classification")
 ```
-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/).