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--- |
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pipeline_tag: sentence-similarity |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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datasets: |
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- clips/mfaq |
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--- |
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# MFAQ |
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This is a FAQ retrieval model, it ranks potential answers according to a given question. It was trained using the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq). |
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## Installation |
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``` |
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pip install sentence-transformers |
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``` |
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## Usage |
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You can use MFAQ with sentence-transformers or directly with a HuggingFace model. |
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In both cases, questions need to be prepended with `<Q>`, and answers with `<A>`. |
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#### Sentence Transformers |
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```python |
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from sentence_transformers import SentenceTransformer |
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question = "<Q>How many models can I host on HuggingFace?" |
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answer_1 = "<A>All plans come with unlimited private models and datasets." |
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answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem." |
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answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job." |
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model = SentenceTransformer('clips/mfaq') |
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embeddings = model.encode([question, answer_1, answer_3, answer_3]) |
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print(embeddings) |
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``` |
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#### HuggingFace Transfoormers |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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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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question = "<Q>How many models can I host on HuggingFace?" |
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answer_1 = "<A>All plans come with unlimited private models and datasets." |
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answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem." |
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answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job." |
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tokenizer = AutoTokenizer.from_pretrained('clips/mfaq') |
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model = AutoModel.from_pretrained('clips/mfaq') |
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# Tokenize sentences |
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encoded_input = tokenizer([question, answer_1, answer_3, answer_3], 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, max pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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``` |