Update README.md

README.md

@@ -1,3 +1,45 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text-classification
 ---
+Usage:
+
+## Model
+
+Base version of e5-v2 finetunned on an annotated subbset of C4 (Numind/C4_sentiment-analysis). This model provide generic embedding for sentiment analysis. 
+
+## Usage
+
+Below is an example to encode text and get embedding.
+
+```python
+import torch.nn.functional as F
+
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+
+model = AutoModel.from_pretrained("Numind/e5-base-SA")
+tokenizer = AutoTokenizer.from_pretrained("Numind/e5-base-SA")
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+
+size = 256
+text = "This movie is amazing"
+
+encoding = tokenizer(
+    text,
+    truncation=True, 
+    padding='max_length', 
+    max_length= size,
+)
+
+emb = model(
+      torch.reshape(torch.tensor(encoding.input_ids),(1,len(encoding.input_ids))).to(device),output_hidden_states=True
+).hidden_states[-1].cpu().detach()
+
+embText = torch.mean(emb,axis = 1)
+
+```