Add new SentenceTransformer model.

README.md

@@ -1,4 +1,5 @@
 ---
+library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
@@ -8,7 +9,7 @@ tags:
 
 ---
 
-# sentence-transformers-testing/stsb-bert-tiny
+# sentence-transformers-testing/stsb-bert-tiny-safetensors
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -28,7 +29,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('sentence-transformers-testing/stsb-bert-tiny')
+model = SentenceTransformer('sentence-transformers-testing/stsb-bert-tiny-safetensors')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +55,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers-testing/stsb-bert-tiny')
-model = AutoModel.from_pretrained('sentence-transformers-testing/stsb-bert-tiny')
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers-testing/stsb-bert-tiny-safetensors')
+model = AutoModel.from_pretrained('sentence-transformers-testing/stsb-bert-tiny-safetensors')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +78,7 @@ print(sentence_embeddings)
 
 <!--- 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=sentence-transformers-testing/stsb-bert-tiny)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers-testing/stsb-bert-tiny-safetensors)
 
 
 ## Training
@@ -97,13 +98,13 @@ The model was trained with the parameters:
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 1,
+    "epochs": 10,
     "evaluation_steps": 1000,
     "evaluator": "NoneType",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
     "optimizer_params": {
-        "lr": 2e-05
+        "lr": 8e-05
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,

config.json

@@ -18,7 +18,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.33.0",
+  "transformers_version": "4.36.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -1,7 +1,7 @@
 {
   "__version__": {
     "sentence_transformers": "2.2.2",
-    "transformers": "4.33.0",
+    "transformers": "4.36.2",
     "pytorch": "2.1.0+cu121"
   }
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3642e25e2379761ee8b18ee545a80506a0d12dbe1499d7312a809865a9b00aec
+oid sha256:b57380d8465cb456819716ab92ba12933f8e9142ae5f930ba18ca830e9333af2
 size 17547912

tokenizer_config.json

@@ -1,4 +1,46 @@
 {
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
   "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
   "do_basic_tokenize": true,