Update README.md

README.md

@@ -1,125 +1,125 @@
----
-license: mit
-language:
-- en
-tags:
-- sentence-embedding
-- sentence-similarity
-- transformers
-- feature-extraction
-pipeline_tag: sentence-similarity
----
-
-# MiniCPM-2B-Text-Embedding-cft
-
-## Description
-
-This is a fine-tuned version of [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) to perform Text Embedding tasks. The model is fine-tuned using the Contrastive Fine-tuning and LoRA technique on NLI datasets.
-
-## Base Model
-
-[MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)
-
-## Usage
-
-1. Clone MiniCPM-2B-dpo-bf16 repository
-
-```bash
-git clone https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16
-```
-
-2. Change a tokenizer setting in `tokenizer_config.json`
-
-```json
-"add_eos_token": true
-```
-
-3. Use the model
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import numpy as np
-
-class MiniCPMSentenceEmbedding:
-    def __init__(self, model_path='openbmb/MiniCPM-2B-dpo-bf16', adapter_path=None):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
-        self.model = AutoModelForCausalLM.from_pretrained(model_path, 
-                                                          torch_dtype=torch.bfloat16,
-                                                          device_map='cuda',
-                                                          trust_remote_code=True)
-        if adapter_path != None:
-            # Load fine-tuned LoRA
-            self.model.load_adapter(adapter_path)
-
-    def get_last_hidden_state(self, text):
-        inputs = self.tokenizer(text, return_tensors="pt").to('cuda')
-        with torch.no_grad():
-            out = self.model(**inputs, output_hidden_states=True).hidden_states[-1][0, -1, :]
-        return out.squeeze().float().cpu().numpy()
-
-    def encode(self, sentences: list[str], **kwargs) -> list[np.ndarray]:
-        """
-        Returns a list of embeddings for the given sentences.
-        
-        Args:
-            sentences: List of sentences to encode
-
-        Returns:
-            List of embeddings for the given sentences
-        """
-
-        out = []
-
-        for s in sentences:
-            out.append(self.get_last_hidden_state(s))
-
-        return out
-
-minicpm_sentence_embedding = PhiSentenceEmbedding(<your-cloned-base-model-path>, 'trapoom555/MiniCPM-2B-Text-Embedding-cft')
-
-example_sentences = ["I don't like apples", "I like apples"]
-
-encoded_sentences = minicpm_sentence_embedding.encode(example_sentences)
-
-print(encoded_sentences) 
-
-```
-
-## Training Details
-
-| **Training Details**    | **Value**         |
-|-------------------------|-------------------|
-| Loss                    | InfoNCE           |
-| Batch Size              | 60                |
-| InfoNCE Temperature     | 0.05              |
-| Learning Rate           | 5e-05             |
-| Warmup Steps            | 100               |
-| Learning Rate Scheduler | CosineAnnealingLR |
-| LoRA Rank               | 8                 |
-| LoRA Alpha              | 32                |
-| LoRA Dropout            | 0.1               |
-| Training Precision      | bf16              |
-| Max Epoch               | 1                 |
-| GPU                     | RTX3090           |
-| Num GPUs                | 4                 |
-
-## Training Scripts
-
-**_(coming soon...)_**
-
-## Checkpoints
-
-We provide checkpoints every 500 training steps which can be found [here](https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints).
-
-## Evaluation Results
-
-**_(coming soon...)_**
-
-## Contributors
-
-Trapoom Ukarapol, Zhicheng Lee, Amy Xin
-
-## Foot Notes
-
+---
+license: mit
+language:
+- en
+tags:
+- sentence-embedding
+- sentence-similarity
+- transformers
+- feature-extraction
+pipeline_tag: sentence-similarity
+---
+
+# MiniCPM-2B-Text-Embedding-cft
+
+## Description
+
+This is a fine-tuned version of [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) to perform Text Embedding tasks. The model is fine-tuned using the Contrastive Fine-tuning and LoRA technique on NLI datasets.
+
+## Base Model
+
+[MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)
+
+## Usage
+
+1. Clone MiniCPM-2B-dpo-bf16 repository
+
+```bash
+git clone https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16
+```
+
+2. Change a tokenizer setting in `tokenizer_config.json`
+
+```json
+"add_eos_token": true
+```
+
+3. Use the model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import numpy as np
+
+class MiniCPMSentenceEmbedding:
+    def __init__(self, model_path='openbmb/MiniCPM-2B-dpo-bf16', adapter_path=None):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForCausalLM.from_pretrained(model_path, 
+                                                          torch_dtype=torch.bfloat16,
+                                                          device_map='cuda',
+                                                          trust_remote_code=True)
+        if adapter_path != None:
+            # Load fine-tuned LoRA
+            self.model.load_adapter(adapter_path)
+
+    def get_last_hidden_state(self, text):
+        inputs = self.tokenizer(text, return_tensors="pt").to('cuda')
+        with torch.no_grad():
+            out = self.model(**inputs, output_hidden_states=True).hidden_states[-1][0, -1, :]
+        return out.squeeze().float().cpu().numpy()
+
+    def encode(self, sentences: list[str], **kwargs) -> list[np.ndarray]:
+        """
+        Returns a list of embeddings for the given sentences.
+        
+        Args:
+            sentences: List of sentences to encode
+
+        Returns:
+            List of embeddings for the given sentences
+        """
+
+        out = []
+
+        for s in sentences:
+            out.append(self.get_last_hidden_state(s))
+
+        return out
+
+minicpm_sentence_embedding = PhiSentenceEmbedding(<your-cloned-base-model-path>, 'trapoom555/MiniCPM-2B-Text-Embedding-cft')
+
+example_sentences = ["I don't like apples", "I like apples"]
+
+encoded_sentences = minicpm_sentence_embedding.encode(example_sentences)
+
+print(encoded_sentences) 
+
+```
+
+## Training Details
+
+| **Training Details**    | **Value**         |
+|-------------------------|-------------------|
+| Loss                    | InfoNCE           |
+| Batch Size              | 60                |
+| InfoNCE Temperature     | 0.05              |
+| Learning Rate           | 5e-05             |
+| Warmup Steps            | 100               |
+| Learning Rate Scheduler | CosineAnnealingLR |
+| LoRA Rank               | 8                 |
+| LoRA Alpha              | 32                |
+| LoRA Dropout            | 0.1               |
+| Training Precision      | bf16              |
+| Max Epoch               | 1                 |
+| GPU                     | RTX3090           |
+| Num GPUs                | 4                 |
+
+## Training Scripts
+
+The training script for this model is written in this [Github repository](https://github.com/trapoom555/Language-Model-STS-CFT/tree/main).
+
+## Checkpoints
+
+We provide checkpoints every 500 training steps which can be found [here](https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints).
+
+## Evaluation Results
+
+**_(coming soon...)_**
+
+## Contributors
+
+Trapoom Ukarapol, Zhicheng Lee, Amy Xin
+
+## Foot Notes
+
 This project is the topic-free final project of the Tsinghua University NLP course for Spring 2024.