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--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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--- |
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# Name of Model |
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<!--- Describe your model here --> |
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## Model Description |
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The model consists of the following layers: |
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(0) Base Transformer Type: RobertaModel |
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(1) mean Pooling |
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## Usage (Sentence-Transformers) |
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Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed: |
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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 sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence"] |
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model = SentenceTransformer('model_name') |
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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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```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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sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) |
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sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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return sum_embeddings / sum_mask |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('model_name') |
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model = AutoModel.from_pretrained('model_name') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, 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, max 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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## Training Procedure |
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<!--- Describe how your model was trained --> |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
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