Update README.md

README.md

@@ -1,143 +1,30 @@
----
-language: []
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-widget: []
-pipeline_tag: sentence-similarity
----
-
-# SentenceTransformer
-
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 1024 tokens
-- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 1024]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## 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.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Framework Versions
-- Python: 3.10.14
-- Sentence Transformers: 3.0.0
-- Transformers: 4.41.2
-- PyTorch: 2.3.1+cu121
-- Accelerate: 0.30.1
-- Datasets: 2.19.2
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+machine[g0271]: /home/duke/shoppal-bge/embedding-sft-output/2024-06-07-12-00-09 
+- source2.3
+  - MRR@1: 0.6451
+  - MRR@3: 0.7159
+  - MRR@5: 0.7289
+  - MRR@10: 0.7372
+  - MRR@100: 0.7405
+  - Recall@1: 0.4601
+  - Recall@3: 0.6209
+  - Recall@5: 0.6955
+  - Recall@10: 0.7937
+  - Recall@100: 0.9731
+  - AUC@100: 0.9231
+  - nDCG@1: 0.6465
+  - nDCG@3: 0.686
+  - nDCG@5: 0.7131
+  - nDCG@10: 0.7479
+  - nDCG@100: 0.8158
+- msmarco
+  - MRR@1: 0.2149
+  - MRR@3: 0.3004
+  - MRR@5: 0.3221
+  - MRR@10: 0.3372
+  - MRR@100: 0.3486
+  - Recall@1: 0.2089
+  - Recall@3: 0.4016
+  - Recall@5: 0.4963
+  - Recall@10: 0.6072
+  - Recall@100: 0.8798
+  - AUC@100: 0.7846