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Update README.md
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README.md
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@@ -19,66 +19,22 @@ Using this model becomes easy when you have [sentence-transformers](https://www.
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model =
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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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.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('pierluigic/xl-lexeme')
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model = AutoModel.from_pretrained('pierluigic/xl-lexeme')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=pierluigic/xl-lexeme)
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## Training
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The model was trained with the parameters:
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```
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pip install -U sentence-transformers
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git clone https://github.com/pierluigic/xl-lexeme
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cd xl-lexeme
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```
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Then you can use the model like this:
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```python
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from WordTransformer import WordTransformer
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model = WordTransformer('pierluigic/xl-lexeme')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Training
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The model was trained with the parameters:
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