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1_Pooling/config.json

@@ -3,5 +3,7 @@
   "pooling_mode_cls_token": false,
   "pooling_mode_mean_tokens": true,
   "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
 }

README.md

@@ -1,24 +1,18 @@
 ---
+library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-license: mit
-datasets:
-- stsb_multi_mt
-- unicamp-dl/mmarco
-language:
-- it
-library_name: sentence-transformers
+
 ---
 
-# {multi-sentence-BERTino}
+# {MODEL_NAME}
 
 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.
 
-This model is trained from  [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) using [mmarco italian](https://huggingface.co/datasets/unicamp-dl/mmarco) (200K) and [stsb italian](https://huggingface.co/datasets/stsb_multi_mt).
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
@@ -33,12 +27,11 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('nickprock/multi-sentence-BERTino')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
-
 ```
 
 
@@ -59,11 +52,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['Una ragazza si acconcia i capelli.', 'Una ragazza si sta spazzolando i capelli.']
+sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('nickprock/multi-sentence-BERTino')
-model = AutoModel.from_pretrained('nickprock/multi-sentence-BERTino')
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +70,6 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 
 print("Sentence embeddings:")
 print(sentence_embeddings)
-
 ```
 
 
@@ -94,7 +86,7 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 15625 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 31250 with parameters:
 ```
 {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
@@ -117,6 +109,20 @@ The model was trained with the parameters:
 
 `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
 
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 31250 with parameters:
+```
+{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
 Parameters of the fit()-Method:
 ```
 {
@@ -140,7 +146,7 @@ Parameters of the fit()-Method:
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
-  (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})
+  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
 )
 ```
 

config_sentence_transformers.json

@@ -1,6 +1,6 @@
 {
   "__version__": {
-    "sentence_transformers": "2.2.2",
+    "sentence_transformers": "2.3.1",
     "transformers": "4.36.0",
     "pytorch": "2.0.0"
   }

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:97427de98b80da98b373899f7fe3e6d5de04f987ec0cc3ad88fb0d4e7d772f03
 size 270316376