Update README.md

README.md

@@ -9,15 +9,6 @@ datasets:
 language:
 - en
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
@@ -32,7 +23,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 
 ## Training Details
-
+https://github.com/mit1280/fined-tuning/blob/main/phi_2_classification_fine_tune.ipynb
 
 
 
@@ -46,16 +37,6 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - training_steps: 10000
-- 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
 
 ### Inference
 
@@ -92,18 +73,12 @@ outputs = model.generate(inputs["input_ids"], max_new_tokens=1024, pad_token_id=
             stopping_criteria = [EosListStoppingCriteria()])
 
 text = tokenizer.batch_decode(outputs)[0]
-
-# print(text.split("The correct option is")[-1].replace("<|im_end|>", "").replace(".", ""))
-
-# Define a dictionary to map values to labels
-label_map = {"2": "positive", "0": "negative", "1": "neutral"}
 answer = text.split("<|im_start|>phi:")[-1].replace("<|im_end|>", "").replace(".", "")
 
 sentiment_label = re.search(r'(\d)', answer)
 sentiment_score = int(sentiment_label.group(1))
 
-if sentiment_label:
-    sentiment_score = int(sentiment_label.group(1))
+if sentiment_score:
     print(id2label.get(sentiment_score, "none"))
 else:
     print("none")