Updating model weights

README.md

@@ -7,7 +7,6 @@ tags:
 - generated_from_trainer
 - dataset_size:556626
 - loss:MultipleNegativesSymmetricRankingLoss
-base_model: sentence-transformers/all-MiniLM-L6-v2
 widget:
 - source_sentence: dimlaj orchid printed finest durable glass terkish tea set
   sentences:
@@ -39,7 +38,7 @@ library_name: sentence-transformers
 metrics:
 - cosine_accuracy
 model-index:
-- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+- name: SentenceTransformer
   results:
   - task:
       type: triplet
@@ -49,19 +48,19 @@ model-index:
       type: unknown
     metrics:
     - type: cosine_accuracy
-      value: 0.9607574939727783
+      value: 0.9618095755577087
       name: Cosine Accuracy
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+# SentenceTransformer
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
 - **Maximum Sequence Length:** 256 tokens
 - **Output Dimensionality:** 384 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -114,9 +113,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.5010, 0.3796],
-#         [0.5010, 1.0000, 0.3538],
-#         [0.3796, 0.3538, 1.0000]])
+# tensor([[1.0000, 0.4517, 0.3474],
+#         [0.4517, 1.0000, 0.3222],
+#         [0.3474, 0.3222, 1.0000]])
 ```
 
 <!--
@@ -153,7 +152,7 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| **cosine_accuracy** | **0.9608** |
+| **cosine_accuracy** | **0.9618** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -228,6 +227,7 @@ You can finetune this model on your own dataset.
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
 - `weight_decay`: 0.001
+- `num_train_epochs`: 6
 - `warmup_steps`: 6956
 - `fp16`: True
 - `dataloader_num_workers`: 2
@@ -258,7 +258,7 @@ You can finetune this model on your own dataset.
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
-- `num_train_epochs`: 3
+- `num_train_epochs`: 6
 - `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
@@ -362,12 +362,11 @@ You can finetune this model on your own dataset.
 </details>
 
 ### Training Logs
-| Epoch  | Step  | Training Loss | Validation Loss | cosine_accuracy |
-|:------:|:-----:|:-------------:|:---------------:|:---------------:|
-| 0.0002 | 1     | 5.3185        | -               | -               |
-| 1.0    | 4349  | 2.6529        | 1.4502          | 0.9492          |
-| 2.0    | 8698  | 1.8024        | 1.3993          | 0.9600          |
-| 3.0    | 13047 | 1.4655        | 1.3219          | 0.9608          |
+| Epoch | Step  | Training Loss | Validation Loss | cosine_accuracy |
+|:-----:|:-----:|:-------------:|:---------------:|:---------------:|
+| 4.0   | 17396 | 1.3564        | 1.3029          | 0.9600          |
+| 5.0   | 21745 | 1.2501        | 1.3017          | 0.9622          |
+| 6.0   | 26094 | 1.1858        | 1.2925          | 0.9618          |
 
 
 ### Framework Versions