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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md CHANGED
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  ---
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- license: apache-2.0
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ # {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 1024 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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+
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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: RobertaModel
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+ (1): Pooling({'word_embedding_dimension': 1024, '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 -->
config.json ADDED
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+ {
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+ "_name_or_path": "benjamin/roberta-large-wechsel-tamil",
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+ "architectures": [
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+ "RobertaModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.19.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 28996
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.2.0",
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+ "transformers": "4.19.2",
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+ "pytorch": "1.11.0+cu113"
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+ }
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+ }
modules.json ADDED
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+ [
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+ {
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+ "idx": 0,
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+ "name": "0",
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+ "path": "",
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+ "type": "sentence_transformers.models.Transformer"
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+ },
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+ {
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+ "idx": 1,
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+ "name": "1",
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+ "path": "1_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]
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sentence_bert_config.json ADDED
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+ {
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+ "max_seq_length": 512,
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+ "do_lower_case": false
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+ }
special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "benjamin/roberta-large-wechsel-tamil", "tokenizer_class": "BertTokenizer"}
vocab.txt ADDED
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