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Upload folder using huggingface_hub

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.gitattributes CHANGED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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README.md ADDED
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+ ---
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+ pipeline_tag: sentence-similarity
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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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+
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+ ---
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+
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+ # {MODEL_NAME}
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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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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+
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+
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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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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+ import torch
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+
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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ print("Sentence embeddings:")
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+ print(sentence_embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
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+ <!--- Describe how your model was evaluated -->
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+
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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={MODEL_NAME})
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+
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+
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+ ## Training
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+ The model was trained with the parameters:
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+
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+ **DataLoader**:
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+
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+ `torch.utils.data.dataloader.DataLoader` of length 497 with parameters:
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+ ```
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+ {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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+ ```
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+
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+ **Loss**:
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+
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+ `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
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+ ```
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+ {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 0.4}
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+ ```
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+
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+ Parameters of the fit()-Method:
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+ ```
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+ {
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+ "epochs": 10,
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+ "evaluation_steps": 100,
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+ "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
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+ "max_grad_norm": 1,
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+ "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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+ "optimizer_params": {
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+ "lr": 2e-05
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+ },
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+ "scheduler": "WarmupLinear",
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+ "steps_per_epoch": null,
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+ "warmup_steps": 497,
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+ "weight_decay": 0.01
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+ }
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+ ```
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+
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+
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+ ## Full Model Architecture
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
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