Update README.md

35c754c 7 months ago

No virus

6.43 kB

	---
	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.

	<!--- 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-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

	<!--- 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 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": "<class 'torch.optim.adamw.AdamW'>",
	"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/)