Rename model

README.md

@@ -9,12 +9,10 @@ tags:
 
 ---
 
-# tomaarsen/mpnet-base-all-nli-matryoshka
+# tomaarsen/mpnet-base-nli-matryoshka
 
 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.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317233cc92fd6fee317e030/TRQAVBKtW6NyTnC5BnTu1.png)
-
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
@@ -31,7 +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('tomaarsen/mpnet-base-all-nli-matryoshka')
+model = SentenceTransformer('tomaarsen/mpnet-base-nli-matryoshka')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -57,8 +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('tomaarsen/mpnet-base-all-nli-matryoshka')
-model = AutoModel.from_pretrained('tomaarsen/mpnet-base-all-nli-matryoshka')
+tokenizer = AutoTokenizer.from_pretrained('tomaarsen/mpnet-base-nli-matryoshka')
+model = AutoModel.from_pretrained('tomaarsen/mpnet-base-nli-matryoshka')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -80,15 +78,50 @@ 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=tomaarsen/mpnet-base-all-nli-matryoshka)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=tomaarsen/mpnet-base-nli-matryoshka)
+
+
+## Training
+The model was trained with the parameters:
 
+**DataLoader**:
+
+`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
+```
+{'batch_size': 64}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
+  ```
+  {'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1]}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "evaluation_steps": 880,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 881,
+    "weight_decay": 0.01
+}
+```
 
 
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (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})
+  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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, 'include_prompt': True})
 )
 ```