danfeg/CAMeL_Base

1---2library_name: sentence-transformers3pipeline_tag: sentence-similarity4tags:5- sentence-transformers6- feature-extraction7- sentence-similarity8- transformers9 10---11 12# danfeg/CAMeL_Base13 14This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.15 16<!--- Describe your model here -->17 18## Usage (Sentence-Transformers)19 20Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:21 22```23pip install -U sentence-transformers24```25 26Then you can use the model like this:27 28```python29from sentence_transformers import SentenceTransformer30sentences = ["This is an example sentence", "Each sentence is converted"]31 32model = SentenceTransformer('danfeg/CAMeL_Base')33embeddings = model.encode(sentences)34print(embeddings)35```36 37 38 39## Usage (HuggingFace Transformers)40Without [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.41 42```python43from transformers import AutoTokenizer, AutoModel44import torch45 46 47#Mean Pooling - Take attention mask into account for correct averaging48def mean_pooling(model_output, attention_mask):49    token_embeddings = model_output[0] #First element of model_output contains all token embeddings50    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()51    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)52 53 54# Sentences we want sentence embeddings for55sentences = ['This is an example sentence', 'Each sentence is converted']56 57# Load model from HuggingFace Hub58tokenizer = AutoTokenizer.from_pretrained('danfeg/CAMeL_Base')59model = AutoModel.from_pretrained('danfeg/CAMeL_Base')60 61# Tokenize sentences62encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')63 64# Compute token embeddings65with torch.no_grad():66    model_output = model(**encoded_input)67 68# Perform pooling. In this case, mean pooling.69sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])70 71print("Sentence embeddings:")72print(sentence_embeddings)73```74 75 76 77## Evaluation Results78 79<!--- Describe how your model was evaluated -->80 81For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=danfeg/CAMeL_Base)82 83 84 85## Full Model Architecture86```87SentenceTransformer(88  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 89  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})90)91```92 93## Citing & Authors94 95<!--- Describe where people can find more information -->