Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 - zero-shot-classification
 - debarta-v3
 model-index:
-- name: distilbert-base-multilingual-cased-sentiments-student
+- name: Softechlb/Sent_analysis_CVs
   results: []
 datasets:
 - tyqiangz/multilingual-sentiments
@@ -30,7 +30,7 @@ language:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# distilbert-base-multilingual-cased-sentiments-student
+# Softechlb/Sent_analysis_CVs
 
 This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment 
 dataset using this [script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation). 
@@ -50,7 +50,7 @@ but we'll pretend and ignore the annotations for the sake of example.
 from transformers import pipeline
 
 distilled_student_sentiment_classifier = pipeline(
-    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", 
+    model="Softechlb/Sent_analysis_CVs", 
     return_all_scores=True
 )
 
@@ -75,43 +75,6 @@ distilled_student_sentiment_classifier("私はこの映画が大好きで、何
 
 ```
 
-
-## Training procedure
-
-Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/Distilling_Zero_Shot_multilingual_distilbert_sentiments_student.ipynb)
-
-### Training hyperparameters
-
-Result can be reproduce using the following commands:
-
-```bash
-python transformers/examples/research_projects/zero-shot-distillation/distill_classifier.py \
---data_file ./multilingual-sentiments/train_unlabeled.txt \
---class_names_file ./multilingual-sentiments/class_names.txt \
---hypothesis_template "The sentiment of this text is {}." \
---teacher_name_or_path MoritzLaurer/mDeBERTa-v3-base-mnli-xnli \
---teacher_batch_size 32 \
---student_name_or_path distilbert-base-multilingual-cased \
---output_dir ./distilbert-base-multilingual-cased-sentiments-student \
---per_device_train_batch_size 16 \
---fp16
-```
-
-If you are training this model on Colab, make the following code changes to avoid Out-of-memory error message:
-```bash
-###### modify L78 to disable fast tokenizer 
-default=False,
-
-###### update dataset map part at L313
-dataset = dataset.map(tokenizer, input_columns="text", fn_kwargs={"padding": "max_length", "truncation": True, "max_length": 512})
-
-###### add following lines to L213
-del model
-print(f"Manually deleted Teacher model, free some memory for student model.")
-
-###### add following lines to L337
-trainer.push_to_hub()
-tokenizer.push_to_hub("distilbert-base-multilingual-cased-sentiments-student")
   
 ```