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1shoomun
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pq_cache_4

Sentence Similarity
sentence-transformers
Safetensors
t5
feature-extraction
Generated from Trainer
dataset_size:7920
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
loss:ContrastiveLoss
custom_code
Eval Results
Model card Files
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Community
1shoomun commited on
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Updated Weights

Browse files
Files changed (26) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +523 -0
  3. checkpoint-2240/1_Pooling/config.json +10 -0
  4. checkpoint-2240/README.md +500 -0
  5. checkpoint-2240/config.json +63 -0
  6. checkpoint-2240/config_sentence_transformers.json +10 -0
  7. checkpoint-2240/model.safetensors +3 -0
  8. checkpoint-2240/modules.json +14 -0
  9. checkpoint-2240/optimizer.pt +3 -0
  10. checkpoint-2240/rng_state.pth +3 -0
  11. checkpoint-2240/scheduler.pt +3 -0
  12. checkpoint-2240/sentence_bert_config.json +4 -0
  13. checkpoint-2240/special_tokens_map.json +125 -0
  14. checkpoint-2240/tokenizer.json +0 -0
  15. checkpoint-2240/tokenizer_config.json +939 -0
  16. checkpoint-2240/trainer_state.json +150 -0
  17. checkpoint-2240/training_args.bin +3 -0
  18. config.json +63 -0
  19. config_sentence_transformers.json +10 -0
  20. eval/Information-Retrieval_evaluation_test-eval_results.csv +0 -0
  21. model.safetensors +3 -0
  22. modules.json +14 -0
  23. sentence_bert_config.json +4 -0
  24. special_tokens_map.json +125 -0
  25. tokenizer.json +0 -0
  26. tokenizer_config.json +939 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:7920
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+ - loss:MultipleNegativesRankingLoss
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+ - loss:CosineSimilarityLoss
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+ - loss:ContrastiveLoss
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+ base_model: jinaai/jina-embedding-b-en-v1
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+ widget:
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+ - source_sentence: Can you tell me how my portfolio did last week?
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+ sentences:
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+ - Show my riskiest holdings
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+ - Suggest recommendations for me
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+ - How did my portfolio perform last week ?
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+ - source_sentence: What suggestions do you have for me?
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+ sentences:
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+ - Suggest recommendations for me
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+ - what are my top stock exposures including my funds
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+ - Whats my portfolio's exposure to X
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+ - source_sentence: Help me see my risk profile
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+ sentences:
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+ - What is the performance of my portfolio over the last week?
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+ - Need to change my risk appetite
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+ - I want to see my risk profile
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+ - source_sentence: Which stock makes up the majority of my portfolio?
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+ sentences:
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+ - Which sector do I invest most in?
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+ - what are my top stock exposures including my funds
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+ - Show me what stock makes up the highest concentration in my portfolio?
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+ - source_sentence: Are there any warning signs in my investments?
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+ sentences:
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+ - How has my portfolio performed over the last year?
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+ - How does this news affect my portfolio?
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+ - List me cheapest funds
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy@1
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+ - cosine_accuracy@3
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+ - cosine_accuracy@5
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+ - cosine_accuracy@10
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+ - cosine_precision@1
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+ - cosine_precision@3
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+ - cosine_precision@5
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+ - cosine_precision@10
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+ - cosine_recall@1
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+ - cosine_recall@3
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+ - cosine_recall@5
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+ - cosine_recall@10
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+ - cosine_ndcg@10
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+ - cosine_mrr@10
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+ - cosine_map@100
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+ model-index:
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+ - name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
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+ results:
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+ - task:
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+ type: information-retrieval
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+ name: Information Retrieval
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+ dataset:
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+ name: test eval
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+ type: test-eval
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+ metrics:
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+ - type: cosine_accuracy@1
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+ value: 0.8409090909090909
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+ name: Cosine Accuracy@1
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+ - type: cosine_accuracy@3
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+ value: 0.9924242424242424
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+ name: Cosine Accuracy@3
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+ - type: cosine_accuracy@5
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+ value: 1.0
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+ name: Cosine Accuracy@5
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+ - type: cosine_accuracy@10
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+ value: 1.0
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+ name: Cosine Accuracy@10
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+ - type: cosine_precision@1
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+ value: 0.8409090909090909
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+ name: Cosine Precision@1
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+ - type: cosine_precision@3
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+ value: 0.3308080808080807
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+ name: Cosine Precision@3
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+ - type: cosine_precision@5
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+ value: 0.19999999999999996
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+ name: Cosine Precision@5
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+ - type: cosine_precision@10
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+ value: 0.09999999999999998
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+ name: Cosine Precision@10
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+ - type: cosine_recall@1
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+ value: 0.8409090909090909
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+ name: Cosine Recall@1
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+ - type: cosine_recall@3
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+ value: 0.9924242424242424
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+ name: Cosine Recall@3
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+ - type: cosine_recall@5
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+ value: 1.0
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+ name: Cosine Recall@5
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+ - type: cosine_recall@10
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+ value: 1.0
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+ name: Cosine Recall@10
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+ - type: cosine_ndcg@10
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+ value: 0.9357996410243691
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+ name: Cosine Ndcg@10
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+ - type: cosine_mrr@10
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+ value: 0.913510101010101
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+ name: Cosine Mrr@10
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+ - type: cosine_map@100
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+ value: 0.913510101010101
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+ name: Cosine Map@100
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+ ---
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+
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+ # SentenceTransformer based on jinaai/jina-embedding-b-en-v1
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
116
+
117
+ ## Model Details
118
+
119
+ ### Model Description
120
+ - **Model Type:** Sentence Transformer
121
+ - **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
132
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
133
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
134
+
135
+ ### Full Model Architecture
136
+
137
+ ```
138
+ SentenceTransformer(
139
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel
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+ (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})
141
+ )
142
+ ```
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+
144
+ ## Usage
145
+
146
+ ### Direct Usage (Sentence Transformers)
147
+
148
+ First install the Sentence Transformers library:
149
+
150
+ ```bash
151
+ pip install -U sentence-transformers
152
+ ```
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+
154
+ Then you can load this model and run inference.
155
+ ```python
156
+ from sentence_transformers import SentenceTransformer
157
+
158
+ # Download from the 🤗 Hub
159
+ model = SentenceTransformer("sentence_transformers_model_id")
160
+ # Run inference
161
+ sentences = [
162
+ 'Are there any warning signs in my investments?',
163
+ 'How has my portfolio performed over the last year?',
164
+ 'How does this news affect my portfolio?',
165
+ ]
166
+ embeddings = model.encode(sentences)
167
+ print(embeddings.shape)
168
+ # [3, 768]
169
+
170
+ # Get the similarity scores for the embeddings
171
+ similarities = model.similarity(embeddings, embeddings)
172
+ print(similarities.shape)
173
+ # [3, 3]
174
+ ```
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+
176
+ <!--
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+ ### Direct Usage (Transformers)
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+
179
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
182
+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
190
+
191
+ </details>
192
+ -->
193
+
194
+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
198
+ -->
199
+
200
+ ## Evaluation
201
+
202
+ ### Metrics
203
+
204
+ #### Information Retrieval
205
+
206
+ * Dataset: `test-eval`
207
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | cosine_accuracy@1 | 0.8409 |
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+ | cosine_accuracy@3 | 0.9924 |
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+ | cosine_accuracy@5 | 1.0 |
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+ | cosine_accuracy@10 | 1.0 |
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+ | cosine_precision@1 | 0.8409 |
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+ | cosine_precision@3 | 0.3308 |
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+ | cosine_precision@5 | 0.2 |
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+ | cosine_precision@10 | 0.1 |
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+ | cosine_recall@1 | 0.8409 |
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+ | cosine_recall@3 | 0.9924 |
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+ | cosine_recall@5 | 1.0 |
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+ | cosine_recall@10 | 1.0 |
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+ | **cosine_ndcg@10** | **0.9358** |
224
+ | cosine_mrr@10 | 0.9135 |
225
+ | cosine_map@100 | 0.9135 |
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+
227
+ <!--
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+ ## Bias, Risks and Limitations
229
+
230
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
231
+ -->
232
+
233
+ <!--
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+ ### Recommendations
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+
236
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
237
+ -->
238
+
239
+ ## Training Details
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+
241
+ ### Training Datasets
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+
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+ #### Unnamed Dataset
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+
245
+ * Size: 1,320 training samples
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+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
247
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
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+ |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.1 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:-------------------------------------------------------|:---------------------------------------------------|:-----------------|
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+ | <code>How does my portfolio score look?</code> | <code>What is my portfolio score?</code> | <code>1.0</code> |
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+ | <code>Show me the risk profile of my portfolio.</code> | <code>Details on my portfolio risk</code> | <code>1.0</code> |
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+ | <code>Which of my shares are the most erratic?</code> | <code>Which of my stocks are most volatile?</code> | <code>1.0</code> |
258
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
259
+ ```json
260
+ {
261
+ "scale": 20.0,
262
+ "similarity_fct": "cos_sim"
263
+ }
264
+ ```
265
+
266
+ #### Unnamed Dataset
267
+
268
+ * Size: 1,320 training samples
269
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
270
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
272
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
273
+ | type | string | string | float |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 10.61 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.09 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence_0 | sentence_1 | label |
277
+ |:-------------------------------------------------------------------|:----------------------------------------------------------------|:-----------------|
278
+ | <code>What holdings carry the least risk in my portfolio?</code> | <code>What are the least risky holdings in my portfolio?</code> | <code>1.0</code> |
279
+ | <code>How have my investments fared over the previous year?</code> | <code>How has my portfolio performed over the last year?</code> | <code>1.0</code> |
280
+ | <code>How well is my portfolio performing?</code> | <code>How is my portfolio performing</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
282
+ ```json
283
+ {
284
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
285
+ }
286
+ ```
287
+
288
+ #### Unnamed Dataset
289
+
290
+ * Size: 5,280 training samples
291
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
292
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
294
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
295
+ | type | string | string | float |
296
+ | details | <ul><li>min: 4 tokens</li><li>mean: 10.65 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.71 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.26</li><li>max: 1.0</li></ul> |
297
+ * Samples:
298
+ | sentence_0 | sentence_1 | label |
299
+ |:---------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------|
300
+ | <code>How much of my portfolio is in X?</code> | <code>What top stocks do I have exposure to</code> | <code>0.0</code> |
301
+ | <code>Can you show me my asset allocation?</code> | <code>Show me what stock makes up the highest concentration in my portfolio?</code> | <code>0.0</code> |
302
+ | <code>What is my portfolio's exposure to X?</code> | <code>What top stocks do I have exposure to</code> | <code>0.0</code> |
303
+ * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
304
+ ```json
305
+ {
306
+ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
307
+ "margin": 0.5,
308
+ "size_average": true
309
+ }
310
+ ```
311
+
312
+ ### Training Hyperparameters
313
+ #### Non-Default Hyperparameters
314
+
315
+ - `eval_strategy`: steps
316
+ - `per_device_train_batch_size`: 32
317
+ - `per_device_eval_batch_size`: 32
318
+ - `num_train_epochs`: 15
319
+ - `multi_dataset_batch_sampler`: round_robin
320
+
321
+ #### All Hyperparameters
322
+ <details><summary>Click to expand</summary>
323
+
324
+ - `overwrite_output_dir`: False
325
+ - `do_predict`: False
326
+ - `eval_strategy`: steps
327
+ - `prediction_loss_only`: True
328
+ - `per_device_train_batch_size`: 32
329
+ - `per_device_eval_batch_size`: 32
330
+ - `per_gpu_train_batch_size`: None
331
+ - `per_gpu_eval_batch_size`: None
332
+ - `gradient_accumulation_steps`: 1
333
+ - `eval_accumulation_steps`: None
334
+ - `torch_empty_cache_steps`: None
335
+ - `learning_rate`: 5e-05
336
+ - `weight_decay`: 0.0
337
+ - `adam_beta1`: 0.9
338
+ - `adam_beta2`: 0.999
339
+ - `adam_epsilon`: 1e-08
340
+ - `max_grad_norm`: 1
341
+ - `num_train_epochs`: 15
342
+ - `max_steps`: -1
343
+ - `lr_scheduler_type`: linear
344
+ - `lr_scheduler_kwargs`: {}
345
+ - `warmup_ratio`: 0.0
346
+ - `warmup_steps`: 0
347
+ - `log_level`: passive
348
+ - `log_level_replica`: warning
349
+ - `log_on_each_node`: True
350
+ - `logging_nan_inf_filter`: True
351
+ - `save_safetensors`: True
352
+ - `save_on_each_node`: False
353
+ - `save_only_model`: False
354
+ - `restore_callback_states_from_checkpoint`: False
355
+ - `no_cuda`: False
356
+ - `use_cpu`: False
357
+ - `use_mps_device`: False
358
+ - `seed`: 42
359
+ - `data_seed`: None
360
+ - `jit_mode_eval`: False
361
+ - `use_ipex`: False
362
+ - `bf16`: False
363
+ - `fp16`: False
364
+ - `fp16_opt_level`: O1
365
+ - `half_precision_backend`: auto
366
+ - `bf16_full_eval`: False
367
+ - `fp16_full_eval`: False
368
+ - `tf32`: None
369
+ - `local_rank`: 0
370
+ - `ddp_backend`: None
371
+ - `tpu_num_cores`: None
372
+ - `tpu_metrics_debug`: False
373
+ - `debug`: []
374
+ - `dataloader_drop_last`: False
375
+ - `dataloader_num_workers`: 0
376
+ - `dataloader_prefetch_factor`: None
377
+ - `past_index`: -1
378
+ - `disable_tqdm`: False
379
+ - `remove_unused_columns`: True
380
+ - `label_names`: None
381
+ - `load_best_model_at_end`: False
382
+ - `ignore_data_skip`: False
383
+ - `fsdp`: []
384
+ - `fsdp_min_num_params`: 0
385
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
386
+ - `tp_size`: 0
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
389
+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
391
+ - `optim`: adamw_torch
392
+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
395
+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
399
+ - `dataloader_pin_memory`: True
400
+ - `dataloader_persistent_workers`: False
401
+ - `skip_memory_metrics`: True
402
+ - `use_legacy_prediction_loop`: False
403
+ - `push_to_hub`: False
404
+ - `resume_from_checkpoint`: None
405
+ - `hub_model_id`: None
406
+ - `hub_strategy`: every_save
407
+ - `hub_private_repo`: None
408
+ - `hub_always_push`: False
409
+ - `gradient_checkpointing`: False
410
+ - `gradient_checkpointing_kwargs`: None
411
+ - `include_inputs_for_metrics`: False
412
+ - `include_for_metrics`: []
413
+ - `eval_do_concat_batches`: True
414
+ - `fp16_backend`: auto
415
+ - `push_to_hub_model_id`: None
416
+ - `push_to_hub_organization`: None
417
+ - `mp_parameters`:
418
+ - `auto_find_batch_size`: False
419
+ - `full_determinism`: False
420
+ - `torchdynamo`: None
421
+ - `ray_scope`: last
422
+ - `ddp_timeout`: 1800
423
+ - `torch_compile`: False
424
+ - `torch_compile_backend`: None
425
+ - `torch_compile_mode`: None
426
+ - `include_tokens_per_second`: False
427
+ - `include_num_input_tokens_seen`: False
428
+ - `neftune_noise_alpha`: None
429
+ - `optim_target_modules`: None
430
+ - `batch_eval_metrics`: False
431
+ - `eval_on_start`: False
432
+ - `use_liger_kernel`: False
433
+ - `eval_use_gather_object`: False
434
+ - `average_tokens_across_devices`: False
435
+ - `prompts`: None
436
+ - `batch_sampler`: batch_sampler
437
+ - `multi_dataset_batch_sampler`: round_robin
438
+
439
+ </details>
440
+
441
+ ### Training Logs
442
+ | Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
443
+ |:------:|:----:|:-------------:|:------------------------:|
444
+ | 1.0 | 126 | - | 0.8744 |
445
+ | 2.0 | 252 | - | 0.8868 |
446
+ | 3.0 | 378 | - | 0.9037 |
447
+ | 3.9683 | 500 | 0.1684 | 0.9159 |
448
+ | 4.0 | 504 | - | 0.9162 |
449
+ | 5.0 | 630 | - | 0.9252 |
450
+ | 6.0 | 756 | - | 0.9232 |
451
+ | 7.0 | 882 | - | 0.9310 |
452
+ | 7.9365 | 1000 | 0.1143 | 0.9358 |
453
+
454
+
455
+ ### Framework Versions
456
+ - Python: 3.10.16
457
+ - Sentence Transformers: 4.1.0
458
+ - Transformers: 4.51.3
459
+ - PyTorch: 2.7.0
460
+ - Accelerate: 1.6.0
461
+ - Datasets: 3.5.0
462
+ - Tokenizers: 0.21.1
463
+
464
+ ## Citation
465
+
466
+ ### BibTeX
467
+
468
+ #### Sentence Transformers
469
+ ```bibtex
470
+ @inproceedings{reimers-2019-sentence-bert,
471
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
472
+ author = "Reimers, Nils and Gurevych, Iryna",
473
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
474
+ month = "11",
475
+ year = "2019",
476
+ publisher = "Association for Computational Linguistics",
477
+ url = "https://arxiv.org/abs/1908.10084",
478
+ }
479
+ ```
480
+
481
+ #### MultipleNegativesRankingLoss
482
+ ```bibtex
483
+ @misc{henderson2017efficient,
484
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
485
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
486
+ year={2017},
487
+ eprint={1705.00652},
488
+ archivePrefix={arXiv},
489
+ primaryClass={cs.CL}
490
+ }
491
+ ```
492
+
493
+ #### ContrastiveLoss
494
+ ```bibtex
495
+ @inproceedings{hadsell2006dimensionality,
496
+ author={Hadsell, R. and Chopra, S. and LeCun, Y.},
497
+ booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
498
+ title={Dimensionality Reduction by Learning an Invariant Mapping},
499
+ year={2006},
500
+ volume={2},
501
+ number={},
502
+ pages={1735-1742},
503
+ doi={10.1109/CVPR.2006.100}
504
+ }
505
+ ```
506
+
507
+ <!--
508
+ ## Glossary
509
+
510
+ *Clearly define terms in order to be accessible across audiences.*
511
+ -->
512
+
513
+ <!--
514
+ ## Model Card Authors
515
+
516
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
517
+ -->
518
+
519
+ <!--
520
+ ## Model Card Contact
521
+
522
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
523
+ -->
checkpoint-2240/1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
checkpoint-2240/README.md ADDED
@@ -0,0 +1,500 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:3570
8
+ - loss:MultipleNegativesRankingLoss
9
+ - loss:CosineSimilarityLoss
10
+ base_model: jinaai/jina-embedding-b-en-v1
11
+ widget:
12
+ - source_sentence: How do I change my stocks to mutual funds?
13
+ sentences:
14
+ - How can I swap my stocks for mutual funds?
15
+ - Show my stocks
16
+ - What are the profits I have gained in my portfolio
17
+ - source_sentence: What percentage of my investments are in large cap?
18
+ sentences:
19
+ - Show some of my best performing holdings
20
+ - Suggest recommendations for me
21
+ - Can you show what percentage of my portfolio consists of large cap
22
+ - source_sentence: How do I change my risk profile?
23
+ sentences:
24
+ - What can I do to bring down the volatility in my portfolio?
25
+ - I want to change my risk profile
26
+ - What is the total value of my portfolio
27
+ - source_sentence: Is now a good time to buy energy stocks considering the war in
28
+ the Middle East and rising fuel prices?
29
+ sentences:
30
+ - Am I investing in the small cap market more?
31
+ - I saw in the news that there is a war going on in the Middle East and fuel will
32
+ be more costly now, should I buy energy sector stocks?
33
+ - Are my ETFs giving better returns compare to my mutual funds?
34
+ - source_sentence: Look for funds that fit my stock holdings
35
+ sentences:
36
+ - Can you tell me if my investments will grow well in the long run?
37
+ - Do I have any stocks in my portfolio?
38
+ - Explore funds that match my stock portfolio
39
+ pipeline_tag: sentence-similarity
40
+ library_name: sentence-transformers
41
+ metrics:
42
+ - cosine_accuracy@1
43
+ - cosine_accuracy@3
44
+ - cosine_accuracy@5
45
+ - cosine_accuracy@10
46
+ - cosine_precision@1
47
+ - cosine_precision@3
48
+ - cosine_precision@5
49
+ - cosine_precision@10
50
+ - cosine_recall@1
51
+ - cosine_recall@3
52
+ - cosine_recall@5
53
+ - cosine_recall@10
54
+ - cosine_ndcg@10
55
+ - cosine_mrr@10
56
+ - cosine_map@100
57
+ model-index:
58
+ - name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
59
+ results:
60
+ - task:
61
+ type: information-retrieval
62
+ name: Information Retrieval
63
+ dataset:
64
+ name: test eval
65
+ type: test-eval
66
+ metrics:
67
+ - type: cosine_accuracy@1
68
+ value: 0.8659217877094972
69
+ name: Cosine Accuracy@1
70
+ - type: cosine_accuracy@3
71
+ value: 0.9916201117318436
72
+ name: Cosine Accuracy@3
73
+ - type: cosine_accuracy@5
74
+ value: 0.9972067039106145
75
+ name: Cosine Accuracy@5
76
+ - type: cosine_accuracy@10
77
+ value: 1.0
78
+ name: Cosine Accuracy@10
79
+ - type: cosine_precision@1
80
+ value: 0.8659217877094972
81
+ name: Cosine Precision@1
82
+ - type: cosine_precision@3
83
+ value: 0.33054003724394787
84
+ name: Cosine Precision@3
85
+ - type: cosine_precision@5
86
+ value: 0.1994413407821229
87
+ name: Cosine Precision@5
88
+ - type: cosine_precision@10
89
+ value: 0.09999999999999999
90
+ name: Cosine Precision@10
91
+ - type: cosine_recall@1
92
+ value: 0.8659217877094972
93
+ name: Cosine Recall@1
94
+ - type: cosine_recall@3
95
+ value: 0.9916201117318436
96
+ name: Cosine Recall@3
97
+ - type: cosine_recall@5
98
+ value: 0.9972067039106145
99
+ name: Cosine Recall@5
100
+ - type: cosine_recall@10
101
+ value: 1.0
102
+ name: Cosine Recall@10
103
+ - type: cosine_ndcg@10
104
+ value: 0.9460695277624867
105
+ name: Cosine Ndcg@10
106
+ - type: cosine_mrr@10
107
+ value: 0.9273743016759775
108
+ name: Cosine Mrr@10
109
+ - type: cosine_map@100
110
+ value: 0.9273743016759777
111
+ name: Cosine Map@100
112
+ ---
113
+
114
+ # SentenceTransformer based on jinaai/jina-embedding-b-en-v1
115
+
116
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
117
+
118
+ ## Model Details
119
+
120
+ ### Model Description
121
+ - **Model Type:** Sentence Transformer
122
+ - **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
123
+ - **Maximum Sequence Length:** 512 tokens
124
+ - **Output Dimensionality:** 768 dimensions
125
+ - **Similarity Function:** Cosine Similarity
126
+ <!-- - **Training Dataset:** Unknown -->
127
+ <!-- - **Language:** Unknown -->
128
+ <!-- - **License:** Unknown -->
129
+
130
+ ### Model Sources
131
+
132
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
133
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
134
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
135
+
136
+ ### Full Model Architecture
137
+
138
+ ```
139
+ SentenceTransformer(
140
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel
141
+ (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})
142
+ )
143
+ ```
144
+
145
+ ## Usage
146
+
147
+ ### Direct Usage (Sentence Transformers)
148
+
149
+ First install the Sentence Transformers library:
150
+
151
+ ```bash
152
+ pip install -U sentence-transformers
153
+ ```
154
+
155
+ Then you can load this model and run inference.
156
+ ```python
157
+ from sentence_transformers import SentenceTransformer
158
+
159
+ # Download from the 🤗 Hub
160
+ model = SentenceTransformer("sentence_transformers_model_id")
161
+ # Run inference
162
+ sentences = [
163
+ 'Look for funds that fit my stock holdings',
164
+ 'Explore funds that match my stock portfolio',
165
+ 'Can you tell me if my investments will grow well in the long run?',
166
+ ]
167
+ embeddings = model.encode(sentences)
168
+ print(embeddings.shape)
169
+ # [3, 768]
170
+
171
+ # Get the similarity scores for the embeddings
172
+ similarities = model.similarity(embeddings, embeddings)
173
+ print(similarities.shape)
174
+ # [3, 3]
175
+ ```
176
+
177
+ <!--
178
+ ### Direct Usage (Transformers)
179
+
180
+ <details><summary>Click to see the direct usage in Transformers</summary>
181
+
182
+ </details>
183
+ -->
184
+
185
+ <!--
186
+ ### Downstream Usage (Sentence Transformers)
187
+
188
+ You can finetune this model on your own dataset.
189
+
190
+ <details><summary>Click to expand</summary>
191
+
192
+ </details>
193
+ -->
194
+
195
+ <!--
196
+ ### Out-of-Scope Use
197
+
198
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
199
+ -->
200
+
201
+ ## Evaluation
202
+
203
+ ### Metrics
204
+
205
+ #### Information Retrieval
206
+
207
+ * Dataset: `test-eval`
208
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
209
+
210
+ | Metric | Value |
211
+ |:--------------------|:-----------|
212
+ | cosine_accuracy@1 | 0.8659 |
213
+ | cosine_accuracy@3 | 0.9916 |
214
+ | cosine_accuracy@5 | 0.9972 |
215
+ | cosine_accuracy@10 | 1.0 |
216
+ | cosine_precision@1 | 0.8659 |
217
+ | cosine_precision@3 | 0.3305 |
218
+ | cosine_precision@5 | 0.1994 |
219
+ | cosine_precision@10 | 0.1 |
220
+ | cosine_recall@1 | 0.8659 |
221
+ | cosine_recall@3 | 0.9916 |
222
+ | cosine_recall@5 | 0.9972 |
223
+ | cosine_recall@10 | 1.0 |
224
+ | **cosine_ndcg@10** | **0.9461** |
225
+ | cosine_mrr@10 | 0.9274 |
226
+ | cosine_map@100 | 0.9274 |
227
+
228
+ <!--
229
+ ## Bias, Risks and Limitations
230
+
231
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
232
+ -->
233
+
234
+ <!--
235
+ ### Recommendations
236
+
237
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
238
+ -->
239
+
240
+ ## Training Details
241
+
242
+ ### Training Datasets
243
+
244
+ #### Unnamed Dataset
245
+
246
+ * Size: 1,785 training samples
247
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
248
+ * Approximate statistics based on the first 1000 samples:
249
+ | | sentence_0 | sentence_1 | label |
250
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
251
+ | type | string | string | float |
252
+ | details | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.11 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
253
+ * Samples:
254
+ | sentence_0 | sentence_1 | label |
255
+ |:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:-----------------|
256
+ | <code>How can I lower the risk in my investments?</code> | <code>How to reduce my risk </code> | <code>1.0</code> |
257
+ | <code>How is my asset allocation divided?</code> | <code>What is my asset allocation breakdown?</code> | <code>1.0</code> |
258
+ | <code>Any specific swap recommendations for better returns?</code> | <code>What are the specific swap suggestions to improve my returns?</code> | <code>1.0</code> |
259
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
260
+ ```json
261
+ {
262
+ "scale": 20.0,
263
+ "similarity_fct": "cos_sim"
264
+ }
265
+ ```
266
+
267
+ #### Unnamed Dataset
268
+
269
+ * Size: 1,785 training samples
270
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
271
+ * Approximate statistics based on the first 1000 samples:
272
+ | | sentence_0 | sentence_1 | label |
273
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
274
+ | type | string | string | float |
275
+ | details | <ul><li>min: 4 tokens</li><li>mean: 11.28 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.98 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
276
+ * Samples:
277
+ | sentence_0 | sentence_1 | label |
278
+ |:----------------------------------------------------------------|:------------------------------------------------------|:-----------------|
279
+ | <code>What should I do to improve my investment returns?</code> | <code>How can I improve my returns?</code> | <code>1.0</code> |
280
+ | <code>Can you give me an overview of my portfolio?</code> | <code>Do you have any insights on my portfolio</code> | <code>1.0</code> |
281
+ | <code>Reveal my stock assets</code> | <code>Show my stocks</code> | <code>1.0</code> |
282
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
283
+ ```json
284
+ {
285
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
286
+ }
287
+ ```
288
+
289
+ ### Training Hyperparameters
290
+ #### Non-Default Hyperparameters
291
+
292
+ - `eval_strategy`: steps
293
+ - `per_device_train_batch_size`: 32
294
+ - `per_device_eval_batch_size`: 32
295
+ - `num_train_epochs`: 20
296
+ - `multi_dataset_batch_sampler`: round_robin
297
+
298
+ #### All Hyperparameters
299
+ <details><summary>Click to expand</summary>
300
+
301
+ - `overwrite_output_dir`: False
302
+ - `do_predict`: False
303
+ - `eval_strategy`: steps
304
+ - `prediction_loss_only`: True
305
+ - `per_device_train_batch_size`: 32
306
+ - `per_device_eval_batch_size`: 32
307
+ - `per_gpu_train_batch_size`: None
308
+ - `per_gpu_eval_batch_size`: None
309
+ - `gradient_accumulation_steps`: 1
310
+ - `eval_accumulation_steps`: None
311
+ - `torch_empty_cache_steps`: None
312
+ - `learning_rate`: 5e-05
313
+ - `weight_decay`: 0.0
314
+ - `adam_beta1`: 0.9
315
+ - `adam_beta2`: 0.999
316
+ - `adam_epsilon`: 1e-08
317
+ - `max_grad_norm`: 1
318
+ - `num_train_epochs`: 20
319
+ - `max_steps`: -1
320
+ - `lr_scheduler_type`: linear
321
+ - `lr_scheduler_kwargs`: {}
322
+ - `warmup_ratio`: 0.0
323
+ - `warmup_steps`: 0
324
+ - `log_level`: passive
325
+ - `log_level_replica`: warning
326
+ - `log_on_each_node`: True
327
+ - `logging_nan_inf_filter`: True
328
+ - `save_safetensors`: True
329
+ - `save_on_each_node`: False
330
+ - `save_only_model`: False
331
+ - `restore_callback_states_from_checkpoint`: False
332
+ - `no_cuda`: False
333
+ - `use_cpu`: False
334
+ - `use_mps_device`: False
335
+ - `seed`: 42
336
+ - `data_seed`: None
337
+ - `jit_mode_eval`: False
338
+ - `use_ipex`: False
339
+ - `bf16`: False
340
+ - `fp16`: False
341
+ - `fp16_opt_level`: O1
342
+ - `half_precision_backend`: auto
343
+ - `bf16_full_eval`: False
344
+ - `fp16_full_eval`: False
345
+ - `tf32`: None
346
+ - `local_rank`: 0
347
+ - `ddp_backend`: None
348
+ - `tpu_num_cores`: None
349
+ - `tpu_metrics_debug`: False
350
+ - `debug`: []
351
+ - `dataloader_drop_last`: False
352
+ - `dataloader_num_workers`: 0
353
+ - `dataloader_prefetch_factor`: None
354
+ - `past_index`: -1
355
+ - `disable_tqdm`: False
356
+ - `remove_unused_columns`: True
357
+ - `label_names`: None
358
+ - `load_best_model_at_end`: False
359
+ - `ignore_data_skip`: False
360
+ - `fsdp`: []
361
+ - `fsdp_min_num_params`: 0
362
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
363
+ - `tp_size`: 0
364
+ - `fsdp_transformer_layer_cls_to_wrap`: None
365
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
366
+ - `deepspeed`: None
367
+ - `label_smoothing_factor`: 0.0
368
+ - `optim`: adamw_torch
369
+ - `optim_args`: None
370
+ - `adafactor`: False
371
+ - `group_by_length`: False
372
+ - `length_column_name`: length
373
+ - `ddp_find_unused_parameters`: None
374
+ - `ddp_bucket_cap_mb`: None
375
+ - `ddp_broadcast_buffers`: False
376
+ - `dataloader_pin_memory`: True
377
+ - `dataloader_persistent_workers`: False
378
+ - `skip_memory_metrics`: True
379
+ - `use_legacy_prediction_loop`: False
380
+ - `push_to_hub`: False
381
+ - `resume_from_checkpoint`: None
382
+ - `hub_model_id`: None
383
+ - `hub_strategy`: every_save
384
+ - `hub_private_repo`: None
385
+ - `hub_always_push`: False
386
+ - `gradient_checkpointing`: False
387
+ - `gradient_checkpointing_kwargs`: None
388
+ - `include_inputs_for_metrics`: False
389
+ - `include_for_metrics`: []
390
+ - `eval_do_concat_batches`: True
391
+ - `fp16_backend`: auto
392
+ - `push_to_hub_model_id`: None
393
+ - `push_to_hub_organization`: None
394
+ - `mp_parameters`:
395
+ - `auto_find_batch_size`: False
396
+ - `full_determinism`: False
397
+ - `torchdynamo`: None
398
+ - `ray_scope`: last
399
+ - `ddp_timeout`: 1800
400
+ - `torch_compile`: False
401
+ - `torch_compile_backend`: None
402
+ - `torch_compile_mode`: None
403
+ - `include_tokens_per_second`: False
404
+ - `include_num_input_tokens_seen`: False
405
+ - `neftune_noise_alpha`: None
406
+ - `optim_target_modules`: None
407
+ - `batch_eval_metrics`: False
408
+ - `eval_on_start`: False
409
+ - `use_liger_kernel`: False
410
+ - `eval_use_gather_object`: False
411
+ - `average_tokens_across_devices`: False
412
+ - `prompts`: None
413
+ - `batch_sampler`: batch_sampler
414
+ - `multi_dataset_batch_sampler`: round_robin
415
+
416
+ </details>
417
+
418
+ ### Training Logs
419
+ | Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
420
+ |:-------:|:----:|:-------------:|:------------------------:|
421
+ | 1.0 | 112 | - | 0.9013 |
422
+ | 2.0 | 224 | - | 0.9112 |
423
+ | 3.0 | 336 | - | 0.9250 |
424
+ | 4.0 | 448 | - | 0.9307 |
425
+ | 4.4643 | 500 | 0.1949 | 0.9337 |
426
+ | 5.0 | 560 | - | 0.9342 |
427
+ | 6.0 | 672 | - | 0.9381 |
428
+ | 7.0 | 784 | - | 0.9423 |
429
+ | 8.0 | 896 | - | 0.9426 |
430
+ | 8.9286 | 1000 | 0.1347 | 0.9452 |
431
+ | 9.0 | 1008 | - | 0.9442 |
432
+ | 10.0 | 1120 | - | 0.9461 |
433
+ | 11.0 | 1232 | - | 0.9461 |
434
+ | 12.0 | 1344 | - | 0.9461 |
435
+ | 13.0 | 1456 | - | 0.9461 |
436
+ | 13.3929 | 1500 | 0.1193 | 0.9461 |
437
+ | 14.0 | 1568 | - | 0.9461 |
438
+ | 15.0 | 1680 | - | 0.9461 |
439
+ | 16.0 | 1792 | - | 0.9461 |
440
+ | 17.0 | 1904 | - | 0.9461 |
441
+ | 17.8571 | 2000 | 0.117 | 0.9461 |
442
+ | 18.0 | 2016 | - | 0.9461 |
443
+ | 19.0 | 2128 | - | 0.9461 |
444
+
445
+
446
+ ### Framework Versions
447
+ - Python: 3.10.16
448
+ - Sentence Transformers: 4.1.0
449
+ - Transformers: 4.51.3
450
+ - PyTorch: 2.7.0
451
+ - Accelerate: 1.6.0
452
+ - Datasets: 3.5.0
453
+ - Tokenizers: 0.21.1
454
+
455
+ ## Citation
456
+
457
+ ### BibTeX
458
+
459
+ #### Sentence Transformers
460
+ ```bibtex
461
+ @inproceedings{reimers-2019-sentence-bert,
462
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
463
+ author = "Reimers, Nils and Gurevych, Iryna",
464
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
465
+ month = "11",
466
+ year = "2019",
467
+ publisher = "Association for Computational Linguistics",
468
+ url = "https://arxiv.org/abs/1908.10084",
469
+ }
470
+ ```
471
+
472
+ #### MultipleNegativesRankingLoss
473
+ ```bibtex
474
+ @misc{henderson2017efficient,
475
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
476
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
477
+ year={2017},
478
+ eprint={1705.00652},
479
+ archivePrefix={arXiv},
480
+ primaryClass={cs.CL}
481
+ }
482
+ ```
483
+
484
+ <!--
485
+ ## Glossary
486
+
487
+ *Clearly define terms in order to be accessible across audiences.*
488
+ -->
489
+
490
+ <!--
491
+ ## Model Card Authors
492
+
493
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
494
+ -->
495
+
496
+ <!--
497
+ ## Model Card Contact
498
+
499
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
500
+ -->
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