Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: BEE-spoke-data/bert-plus-L8-4096-v1.0
+base_model: BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
@@ -9,27 +9,31 @@ tags:
 - transformers
 license: apache-2.0
 widget:
-  - source_sentence: "How to discreetly optimize operating expenses?"
-    sentences:
-      - "Strategies for quietly reducing overhead costs"
-      - "Subtle ways to cut down on operating expenses"
-      - "Implementing technology to save on operating costs without broad announcements"
-      - "Lowering daily business expenses through unnoticed efficiencies"
-      - "Minimizing operational expenditures in small businesses without drawing attention"
-
-
+- source_sentence: How to discreetly optimize operating expenses?
+  sentences:
+  - Strategies for quietly reducing overhead costs
+  - Subtle ways to cut down on operating expenses
+  - >-
+    Implementing technology to save on operating costs without broad
+    announcements
+  - Lowering daily business expenses through unnoticed efficiencies
+  - >-
+    Minimizing operational expenditures in small businesses without drawing
+    attention
+datasets:
+- pszemraj/synthetic-text-similarity
+language:
+- en
 ---
-
-# BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka
-
+# BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k
 
 
 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 was finetuned at 512 ctx (allNLI is all short-ctx examples) but the base model supports 4096
+- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k) on 'v3.0' of the `synthetic text similarity' dataset.
+- Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
 - Matryoshka dims: [768, 512, 256, 128, 64]
 
-<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
@@ -45,7 +49,7 @@ 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('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
+model = SentenceTransformer('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -71,8 +75,8 @@ 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('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
-model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka')
+tokenizer = AutoTokenizer.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
+model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -88,55 +92,13 @@ 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=BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka)
-
-
 ## Training
-The model was trained with the parameters:
 
-**DataLoader**:
-
-`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8663 with parameters:
-```
-{'batch_size': 32}
-```
+See training details below.
 
 **Loss**:
 
 `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
   ```
-  {'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 216,
-    "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": 867,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (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})
-)
-```
+  {'loss': 'CosineSimilarityLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
+  ```