Update README.md

README.md

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+
 ---
 tags:
 - sentence-transformers
@@ -27,40 +28,7 @@ model-index:
       name: Spearman Cosine
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
-
-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 32-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 -->
-- **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 32 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 32, '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})
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
+# Super small embedding model (only 4MB!)
 
 First install the Sentence Transformers library:
 
@@ -95,90 +63,3 @@ print(similarities)
 #        [0.2864, 0.2265, 1.0000]])
 ```
 
-<!--
-### Direct Usage (Transformers)
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-<details><summary>Click to see the direct usage in Transformers</summary>
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-</details>
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-<!--
-### Downstream Usage (Sentence Transformers)
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-You can finetune this model on your own dataset.
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-<details><summary>Click to expand</summary>
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-</details>
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-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-
-## Evaluation
-
-### Metrics
-
-#### Semantic Similarity
-
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.6752     |
-| **spearman_cosine** | **0.7044** |
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-<!--
-### Recommendations
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-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-## Training Details
-
-### Training Logs
-| Epoch | Step | spearman_cosine |
-|:-----:|:----:|:---------------:|
-| -1    | -1   | 0.7044          |
-
-
-### Framework Versions
-- Python: 3.10.10
-- Sentence Transformers: 4.1.0
-- Transformers: 4.51.3
-- PyTorch: 2.7.0+cu128
-- Accelerate: 
-- Datasets: 3.5.1
-- Tokenizers: 0.21.1
-
-## Citation
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-### BibTeX
-
-<!--
-## Glossary
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-*Clearly define terms in order to be accessible across audiences.*
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-<!--
-## Model Card Authors
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-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-<!--
-## Model Card Contact
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-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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