
# MMARCO-bert-base-italian-uncased

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.

<!--- Describe your model here -->

## 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-bert-base-italian-uncased')

#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-bert-base-italian-uncased")
model = AutoModel.from_pretrained("nickprock/mmarco-bert-base-italian-uncased")

#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

<!--- Describe how your model was evaluated -->

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 6250 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": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 1500,
    "warmup_steps": 6250,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->
{"language": ["it"], "license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "datasets": ["unicamp-dl/mmarco"], "pipeline_tag": "sentence-similarity", "region": "Italy", "model-index": [{"name": "mmarco-bert-base-italian-uncased", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB MassiveIntentClassification (it)", "type": "mteb/amazon_massive_intent", "config": "it", "split": "test", "revision": "31efe3c427b0bae9c22cbb560b8f15491cc6bed7"}, "metrics": [{"type": "accuracy", "value": 55.06052454606589}, {"type": "f1", "value": 54.014768121214104}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB MassiveScenarioClassification (it)", "type": "mteb/amazon_massive_scenario", "config": "it", "split": "test", "revision": "7d571f92784cd94a019292a1f45445077d0ef634"}, "metrics": [{"type": "accuracy", "value": 63.04303967720243}, {"type": "f1", "value": 62.695230714417406}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB STS22 (it)", "type": "mteb/sts22-crosslingual-sts", "config": "it", "split": "test", "revision": "6d1ba47164174a496b7fa5d3569dae26a6813b80"}, "metrics": [{"type": "cos_sim_pearson", "value": 64.73840574137837}, {"type": "cos_sim_spearman", "value": 69.44233124548987}, {"type": "euclidean_pearson", "value": 67.65045364124317}, {"type": "euclidean_spearman", "value": 69.586510471675}, {"type": "manhattan_pearson", "value": 67.76125181623837}, {"type": "manhattan_spearman", "value": 69.61010945802974}]}]}]}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers mteb it dataset:unicamp-dl/mmarco license:mit model-index endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #mteb #it #dataset-unicamp-dl/mmarco #license-mit #model-index #endpoints_compatible #region-us 


# MMARCO-bert-base-italian-uncased

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 installed:



Then you can use the model like this:





## Usage (HuggingFace Transformers)
Without sentence-transformers, 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.





## Evaluation Results



For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL


## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 6250 with parameters:


Loss:

'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:
  

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors


# MMARCO-bert-base-italian-uncased

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, 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. ## Evaluation Results



For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 6250 with parameters:


Loss:

'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #mteb #it #dataset-unicamp-dl/mmarco #license-mit #model-index #endpoints_compatible #region-us 
 # MMARCO-bert-base-italian-uncased

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, 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. ## Evaluation Results



For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 6250 with parameters:


Loss:

'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors