Update README.md

README.md

@@ -20,28 +20,13 @@ widget:
     học Bách khoa Hà Nội giúp họ thích nghi nhanh chóng.
   - Hà Nội có khí hậu mát mẻ vào mùa thu.
   - Các món ăn ở Hà Nội rất ngon và đa dạng.
-license: apache-2.0
 ---
 
-# bkai-foundation-models/vietnamese-bi-encoder
+# NghiemAbe/sami-sbert-CT
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-We train the model on a merged training dataset that consists of: 
-  - MS Macro (translated into Vietnamese)
-  - SQuAD v2  (translated into Vietnamese)
-  - 80% of the training set from the Legal Text Retrieval Zalo 2021 challenge
-
-We use [phobert-base-v2](https://github.com/VinAIResearch/PhoBERT) as the pre-trained backbone.
-
-Here are the results on the remaining 20% of the training set from the Legal Text Retrieval Zalo 2021 challenge:
-
-|     Pretrained Model          |     Trained Datasets                  |     Acc@1    |     Acc@10    |     Acc@100    |     Pre@10    |     MRR@10    |
-|-------------------------------|---------------------------------------|:------------:|:-------------:|:--------------:|:-------------:|:-------------:|
-|     [Vietnamese-SBERT](https://huggingface.co/keepitreal/vietnamese-sbert)     |     -                                 |     32.34    |      52.97    |      89.84     |      7.05     |      45.30    |
-|     PhoBERT-base-v2           |     MSMACRO                           |     47.81    |      77.19    |      92.34     |      7.72     |      58.37    |
-|     PhoBERT-base-v2                            |     MSMACRO + SQuADv2.0 + 80% Zalo    |     73.28    |      93.59    |      98.85     |      9.36     |      80.73    |
-
+I use pretrained model [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) and train the model on SAMI dataset.
 
 <!--- Describe your model here -->
 
@@ -61,94 +46,11 @@ from sentence_transformers import SentenceTransformer
 # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
 sentences = ["Cô ấy là một người vui_tính .", "Cô ấy cười nói suốt cả ngày ."]
 
-model = SentenceTransformer('bkai-foundation-models/vietnamese-bi-encoder')
+model = SentenceTransformer('NghiemAbe/sami-sbert-CT')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-## Usage (Widget HuggingFace)
-The widget use custom pipeline on top of the default pipeline by adding additional word segmenter before PhobertTokenizer. So you do not need to segment words before using the API:
-
-An example could be seen in Hosted inference API.
- 
-
-## Usage (HuggingFace Transformers)
-
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings, we could use pyvi, underthesea, RDRSegment to segment words
-sentences = ['Cô ấy là một người vui_tính .', 'Cô ấy cười nói suốt cả ngày .']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('bkai-foundation-models/vietnamese-bi-encoder')
-model = AutoModel.from_pretrained('bkai-foundation-models/vietnamese-bi-encoder')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-## Training
-
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 17584 with parameters:
-
-```
-{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-
-```
-{'scale': 20.0, 'similarity_fct': 'cos_sim'}
-```
-
-Parameters of the fit()-Method:
-
-```
-{
-    "epochs": 15,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 1000,
-    "weight_decay": 0.01
-}
-```
-
 ## Full Model Architecture
 
 ```