Updating model weights

README.md

@@ -7,7 +7,6 @@ tags:
 - generated_from_trainer
 - dataset_size:291522
 - loss:MultipleNegativesSymmetricRankingLoss
-base_model: sentence-transformers/all-MiniLM-L6-v2
 widget:
 - source_sentence: cream 21 baby oil with almond oil
   sentences:
@@ -38,32 +37,17 @@ widget:
   - kids game
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
-metrics:
-- cosine_accuracy
-model-index:
-- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
-  results:
-  - task:
-      type: triplet
-      name: Triplet
-    dataset:
-      name: Unknown
-      type: unknown
-    metrics:
-    - type: cosine_accuracy
-      value: 0.9412940740585327
-      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
@@ -145,18 +129,6 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
-## Evaluation
-
-### Metrics
-
-#### Triplet
-
-* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| **cosine_accuracy** | **0.9413** |
-
 <!--
 ## Bias, Risks and Limitations
 
@@ -226,7 +198,7 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `eval_strategy`: steps
+- `eval_strategy`: epoch
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
 - `weight_decay`: 0.001
@@ -245,7 +217,7 @@ You can finetune this model on your own dataset.
 
 - `overwrite_output_dir`: False
 - `do_predict`: False
-- `eval_strategy`: steps
+- `eval_strategy`: epoch
 - `prediction_loss_only`: True
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
@@ -363,13 +335,6 @@ You can finetune this model on your own dataset.
 
 </details>
 
-### Training Logs
-| Epoch  | Step | Training Loss | Validation Loss | cosine_accuracy |
-|:------:|:----:|:-------------:|:---------------:|:---------------:|
-| 0.0004 | 1    | 5.3655        | -               | -               |
-| 2.1949 | 5000 | 2.1423        | 0.7694          | 0.9413          |
-
-
 ### Framework Versions
 - Python: 3.11.13
 - Sentence Transformers: 5.1.2