Update README.md

README.md

@@ -1,3 +1,81 @@
 ---
 license: mit
 ---
+
+---
+tags:
+- sentence-transformers
+- feature-extraction
+---
+# Name of Model
+
+<!--- Describe your model here -->
+
+## Model Description
+The model consists of the following layers:
+
+(0) Base Transformer Type: BAAI/bge-small-en-v1.5
+
+(1) mean Pooling
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence"]
+model = SentenceTransformer('model_name')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+## Usage (HuggingFace Transformers)
+
+```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()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence']
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('model_name')
+model = AutoModel.from_pretrained('model_name')
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Training Procedure
+
+<!--- Describe how your model was trained -->
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->