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--- |
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model-index: |
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- name: mmarco-bert-base-italian-uncased |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_intent |
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name: MTEB MassiveIntentClassification (it) |
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config: it |
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split: test |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
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metrics: |
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- type: accuracy |
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value: 55.06052454606589 |
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- type: f1 |
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value: 54.014768121214104 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_scenario |
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name: MTEB MassiveScenarioClassification (it) |
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config: it |
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split: test |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
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metrics: |
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- type: accuracy |
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value: 63.04303967720243 |
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- type: f1 |
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value: 62.695230714417406 |
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- task: |
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type: STS |
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dataset: |
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type: mteb/sts22-crosslingual-sts |
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name: MTEB STS22 (it) |
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config: it |
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split: test |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
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metrics: |
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- type: cos_sim_pearson |
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value: 64.73840574137837 |
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- type: cos_sim_spearman |
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value: 69.44233124548987 |
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- type: euclidean_pearson |
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value: 67.65045364124317 |
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- type: euclidean_spearman |
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value: 69.586510471675 |
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- type: manhattan_pearson |
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value: 67.76125181623837 |
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- type: manhattan_spearman |
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value: 69.61010945802974 |
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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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- mteb |
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license: mit |
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datasets: |
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- unicamp-dl/mmarco |
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language: |
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- it |
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library_name: sentence-transformers |
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region: Italy |
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--- |
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# MMARCO-bert-base-italian-uncased |
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This 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. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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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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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer, util |
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query = "Quante persone vivono a Londra?" |
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docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] |
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#Load the model |
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model = SentenceTransformer('nickprock/mmarco-bert-base-italian-uncased') |
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#Encode query and documents |
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query_emb = model.encode(query) |
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doc_emb = model.encode(docs) |
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#Compute dot score between query and all document embeddings |
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scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() |
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#Combine docs & scores |
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doc_score_pairs = list(zip(docs, scores)) |
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#Sort by decreasing score |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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#Output passages & scores |
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for doc, score in doc_score_pairs: |
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print(score, doc) |
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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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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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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.last_hidden_state |
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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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#Encode text |
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def encode(texts): |
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# Tokenize sentences |
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encoded_input = tokenizer(texts, 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, return_dict=True) |
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# Perform pooling |
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embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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return embeddings |
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# Sentences we want sentence embeddings for |
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query = "Quante persone vivono a Londra?" |
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docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-bert-base-italian-uncased") |
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model = AutoModel.from_pretrained("nickprock/mmarco-bert-base-italian-uncased") |
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#Encode query and docs |
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query_emb = encode(query) |
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doc_emb = encode(docs) |
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#Compute dot score between query and all document embeddings |
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scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() |
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#Combine docs & scores |
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doc_score_pairs = list(zip(docs, scores)) |
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#Sort by decreasing score |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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#Output passages & scores |
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print("Query:", query) |
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for doc, score in doc_score_pairs: |
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print(score, doc) |
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``` |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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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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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 6250 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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**Loss**: |
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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': 5} |
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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": 500, |
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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": 1500, |
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"warmup_steps": 6250, |
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"weight_decay": 0.01 |
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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': 768, '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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## Citing & Authors |
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<!--- Describe where people can find more information --> |
