--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb license: apache-2.0 datasets: - unicamp-dl/mmarco language: - it library_name: sentence-transformers model-index: - name: mmarco-sentence-flare-it results: - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (it) config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 22.299932750504368 - type: f1 value: 20.147804322480262 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (it) config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 27.40753194351042 - type: f1 value: 25.187141587127705 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (it) config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 30.67175493186678 - type: cos_sim_spearman value: 37.92638638971281 - type: euclidean_pearson value: 37.47072224334179 - type: euclidean_spearman value: 39.23036609148336 - type: manhattan_pearson value: 42.92657347688227 - type: manhattan_spearman value: 43.93955531904934 --- # mmarco-sentence-flare-it 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. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "Quante persone vivono a Londra?" docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] #Load the model model = SentenceTransformer('nickprock/mmarco-sentence-flare-it') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) 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. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) return embeddings # Sentences we want sentence embeddings for query = "Quante persone vivono a Londra?" docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-sentence-flare-it") model = AutoModel.from_pretrained("nickprock/mmarco-sentence-flare-it") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores print("Query:", query) for doc, score in doc_score_pairs: print(score, doc) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 7500 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1500, "warmup_steps": 7500, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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}) ) ``` ## Citing & Authors More information about the [base model here](https://huggingface.co./osiria/flare-it/)