Pushing model

Model trained on SAMI dataset using CT loss

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+library_name: generic
+language:
+- vi
+widget:
+- source_sentence: Làm thế nào Đại học Bách khoa Hà Nội thu hút sinh viên quốc tế?
+  sentences:
+  - >-
+    Đại học Bách khoa Hà Nội đã phát triển các chương trình đào tạo bằng tiếng
+    Anh để làm cho việc học tại đây dễ dàng hơn cho sinh viên quốc tế.
+  - >-
+    Môi trường học tập đa dạng và sự hỗ trợ đầy đủ cho sinh viên quốc tế tại Đại
+    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
+
+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    |
+
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+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')
+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
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
+)
+```