Upload README.md

README.md

@@ -90,41 +90,6 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-### 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 for
-sentences = ["Kini olu ilu England", "Kini eranko ti o gbona julọ ni agbaye?"]
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('0xnu/pmmlv2-fine-tuned-yoruba')
-model = AutoModel.from_pretrained('0xnu/pmmlv2-fine-tuned-yoruba')
-
-# 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, max pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
 ### License
 
 This project is licensed under the [MIT License](./LICENSE).