Update lm_finetuning.py

lm_finetuning.py

@@ -35,10 +35,21 @@ from datasets import load_dataset, load_metric
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
 from ray import tune
 
+from readme import get_readme
+
 logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')
 
 PARALLEL = bool(int(os.getenv("PARALLEL", 1)))
 RAY_RESULTS = os.getenv("RAY_RESULTS", "ray_results")
+LABEL2ID = {
+        "arts_&_culture": 0,
+        "business_&_entrepreneurs": 1,
+        "pop_culture": 2,
+        "daily_life": 3,
+        "sports_&_gaming": 4,
+        "science_&_technology": 5
+    }
+ID2LABEL = {v: k for k, v in LABEL2ID.items()}
 
 
 def internet_connection(host='http://google.com'):
@@ -96,7 +107,12 @@ def main():
     # setup model
     tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
     model = AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=6, local_files_only=not network)
+        opt.model,
+        num_labels=6,
+        local_files_only=not network,
+        id2label=ID2LABEL,
+        label2id=LABEL2ID
+        )
     tokenized_datasets = dataset.map(
         lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=opt.seq_length),
         batched=True)
@@ -116,7 +132,14 @@ def main():
             train_dataset=tokenized_datasets[opt.split_train],
             eval_dataset=tokenized_datasets[opt.split_validation],
             compute_metrics=compute_metric_search,
-            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(opt.model, num_labels=6, local_files_only=not network, return_dict=True)
+            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
+                opt.model,
+                num_labels=6,
+                local_files_only=not network,
+                return_dict=True,
+                id2label=ID2LABEL,
+                label2id=LABEL2ID
+            )
         )
         # parameter search
         if PARALLEL:
@@ -150,7 +173,12 @@ def main():
 
     # evaluation
     model = AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=6, local_files_only=not network)
+        opt.model,
+        num_labels=6,
+        local_files_only=not network,
+        id2label=ID2LABEL,
+        label2id=LABEL2ID
+    )
     trainer = Trainer(
         model=model,
         args=TrainingArguments(
@@ -162,7 +190,13 @@ def main():
         eval_dataset=tokenized_datasets[opt.split_test],
         compute_metrics=compute_metric_all,
         model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=6, local_files_only=not network, return_dict=True)
+            opt.model,
+            num_labels=6,
+            local_files_only=not network,
+            return_dict=True,
+            id2label=ID2LABEL,
+            label2id=LABEL2ID
+        )
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval:
@@ -181,6 +215,15 @@ def main():
         tokenizer.push_to_hub(opt.model_alias, **args)
         if os.path.exists(summary_file):
             shutil.copy2(summary_file, opt.model_alias)
+        extra_desc = f"This model is fine-tuned on `{opt.split_train}` split and validated on `{opt.split_test}` split of tweet_topic."
+        readme = get_readme(
+            model_name=opt.model_alias,
+            metric=f"{opt.model_alias}/{summary_file}",
+            language_model=opt.model,
+            extra_desc= extra_desc
+            )
+        with open(f"{opt.model_alias}/README.md", "w") as f:
+            f.write(readme)
         os.system(
             f"cd {opt.model_alias} && git lfs install && git add . && git commit -m 'model update' && git push && cd ../")
         shutil.rmtree(f"{opt.model_alias}")  # clean up the cloned repo