Update README.md

README.md

@@ -1,5 +1,7 @@
 ---
 pipeline_tag: sentence-similarity
+language: 
+  - it
 tags:
 - sentence-transformers
 - feature-extraction
@@ -7,11 +9,9 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# sentence-IT5-small
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 ([IT5](https://huggingface.co/gsarti/it5-small)) small model trained for asymmetric semantic search. Query is a keyword, Paragraph is a short news article.
 
 ## Usage (Sentence-Transformers)
 
@@ -25,9 +25,9 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('efederici/sentence-IT5-small')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -50,11 +50,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-IT5-small')
+model = AutoModel.from_pretrained('efederici/sentence-IT5-small')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -70,51 +70,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## 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 1250 with parameters:
-```
-{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 300,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 125,
-    "weight_decay": 0.01
-}
-```
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -122,7 +77,3 @@ SentenceTransformer(
   (1): Pooling({'word_embedding_dimension': 512, '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 -->