tomaarsen HF staff commited on
Commit
59ef6f2
1 Parent(s): 38f57e2

Add new SentenceTransformer model.

Browse files
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0_WordEmbeddings/whitespacetokenizer_config.json ADDED
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+ {
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+ "tokenizer_class": "sentence_transformers.models.tokenizer.WhitespaceTokenizer.WhitespaceTokenizer",
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+ "update_embeddings": false,
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+ "max_seq_length": 1000000
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+ }
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+ {
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+ "word_embedding_dimension": 300,
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+ "dropout": 0,
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+ "bidirectional": true
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+ {
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+ "word_embedding_dimension": 2048,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "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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+ language:
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+ - en
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+ library_name: sentence-transformers
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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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+ - loss:CosineSimilarityLoss
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ widget:
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+ - source_sentence: A man is spitting.
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+ sentences:
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+ - A man is crying.
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+ - Bombings kill 19 people in Iraq
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+ - Three women are sitting near a wall.
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+ - source_sentence: A plane in the sky.
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+ sentences:
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+ - Two airplanes in the sky.
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+ - Suicide bomber strikes in Syria
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+ - Two women posing with a baby.
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+ - source_sentence: A woman is reading.
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+ sentences:
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+ - A woman is writing something.
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+ - Some cyclists stop near a sign.
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+ - Someone is greating a carrot.
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+ - source_sentence: A man is speaking.
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+ sentences:
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+ - A man is talking.
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+ - Bombings kill 19 people in Iraq
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+ - Kittens are eating food on trays.
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+ - source_sentence: a woman has a child.
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+ sentences:
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+ - A pregnant woman is in labor
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+ - Some cyclists stop near a sign.
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+ - Someone is stirring chili in a kettle.
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+ pipeline_tag: sentence-similarity
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+ co2_eq_emissions:
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+ emissions: 0.17244918455341185
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+ energy_consumed: 0.0004436539677012515
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+ source: codecarbon
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+ training_type: fine-tuning
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+ on_cloud: false
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+ cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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+ ram_total_size: 31.777088165283203
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+ hours_used: 0.003
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+ hardware_used: 1 x NVIDIA GeForce RTX 3090
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+ model-index:
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+ - name: SentenceTransformer
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev
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+ type: sts-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7708672762349984
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7657600316758283
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.7474564039693722
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.75228158575576
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.7489387720530025
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.7541126864285251
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.6124844196169514
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.6662313602123413
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.7708672762349984
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.7657600316758283
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 1000000 tokens
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+ - **Output Dimensionality:** 2048 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
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+ - **Language:** en
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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)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
125
+ ```
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+ SentenceTransformer(
127
+ (0): WordEmbeddings(
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+ (emb_layer): Embedding(400001, 300)
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+ )
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+ (1): LSTM(
131
+ (encoder): LSTM(300, 1024, batch_first=True, bidirectional=True)
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+ )
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+ (2): Pooling({'word_embedding_dimension': 2048, '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})
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+ )
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+ ```
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+
137
+ ## Usage
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+
139
+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
143
+ ```bash
144
+ pip install -U sentence-transformers
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+ ```
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+
147
+ Then you can load this model and run inference.
148
+ ```python
149
+ from sentence_transformers import SentenceTransformer
150
+
151
+ # Download from the 🤗 Hub
152
+ model = SentenceTransformer("tomaarsen/glove-bilstm-sts")
153
+ # Run inference
154
+ sentences = [
155
+ 'a woman has a child.',
156
+ 'A pregnant woman is in labor',
157
+ 'Some cyclists stop near a sign.',
158
+ ]
159
+ embeddings = model.encode(sentences)
160
+ print(embeddings.shape)
161
+ # [3, 2048]
162
+
163
+ # Get the similarity scores for the embeddings
164
+ similarities = model.similarity(embeddings)
165
+ print(similarities.shape)
166
+ # [3, 3]
167
+ ```
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+
169
+ <!--
170
+ ### Direct Usage (Transformers)
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+
172
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
174
+ </details>
175
+ -->
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+
177
+ <!--
178
+ ### Downstream Usage (Sentence Transformers)
179
+
180
+ You can finetune this model on your own dataset.
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+
182
+ <details><summary>Click to expand</summary>
183
+
184
+ </details>
185
+ -->
186
+
187
+ <!--
188
+ ### Out-of-Scope Use
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+
190
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
191
+ -->
192
+
193
+ ## Evaluation
194
+
195
+ ### Metrics
196
+
197
+ #### Semantic Similarity
198
+ * Dataset: `sts-dev`
199
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.7709 |
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+ | **spearman_cosine** | **0.7658** |
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+ | pearson_manhattan | 0.7475 |
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+ | spearman_manhattan | 0.7523 |
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+ | pearson_euclidean | 0.7489 |
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+ | spearman_euclidean | 0.7541 |
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+ | pearson_dot | 0.6125 |
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+ | spearman_dot | 0.6662 |
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+ | pearson_max | 0.7709 |
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+ | spearman_max | 0.7658 |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
217
+ *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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+ -->
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+
220
+ <!--
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+ ### Recommendations
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+
223
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
225
+
226
+ ## Training Details
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+
228
+ ### Training Dataset
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+
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+ #### sentence-transformers/stsb
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+
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+ * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
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+ * Size: 5,749 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 1 tokens</li><li>mean: 3.38 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 3.39 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
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+ | <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
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+ | <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
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+ | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
248
+ {
249
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
250
+ }
251
+ ```
252
+
253
+ ### Evaluation Dataset
254
+
255
+ #### sentence-transformers/stsb
256
+
257
+ * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
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+ * Size: 1,500 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
260
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
262
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
263
+ | type | string | string | float |
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+ | details | <ul><li>min: 1 tokens</li><li>mean: 5.17 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 5.08 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
267
+ |:--------------------------------------------------|:------------------------------------------------------|:------------------|
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+ | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
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+ | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
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+ | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
273
+ {
274
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
275
+ }
276
+ ```
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+
278
+ ### Training Hyperparameters
279
+ #### Non-Default Hyperparameters
280
+
281
+ - `eval_strategy`: steps
282
+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `num_train_epochs`: 1
285
+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
287
+
288
+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
290
+
291
+ - `overwrite_output_dir`: False
292
+ - `do_predict`: False
293
+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: False
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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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}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
356
+ - `optim_args`: None
357
+ - `adafactor`: False
358
+ - `group_by_length`: False
359
+ - `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`: None
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
373
+ - `gradient_checkpointing`: False
374
+ - `gradient_checkpointing_kwargs`: None
375
+ - `include_inputs_for_metrics`: False
376
+ - `eval_do_concat_batches`: True
377
+ - `fp16_backend`: auto
378
+ - `push_to_hub_model_id`: None
379
+ - `push_to_hub_organization`: None
380
+ - `mp_parameters`:
381
+ - `auto_find_batch_size`: False
382
+ - `full_determinism`: False
383
+ - `torchdynamo`: None
384
+ - `ray_scope`: last
385
+ - `ddp_timeout`: 1800
386
+ - `torch_compile`: False
387
+ - `torch_compile_backend`: None
388
+ - `torch_compile_mode`: None
389
+ - `dispatch_batches`: None
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+ - `split_batches`: None
391
+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
398
+ </details>
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+
400
+ ### Training Logs
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+ | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
402
+ |:------:|:----:|:-------------:|:------:|:-----------------------:|
403
+ | 0.5556 | 100 | 0.0809 | 0.0566 | 0.7658 |
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+
405
+
406
+ ### Environmental Impact
407
+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
408
+ - **Energy Consumed**: 0.000 kWh
409
+ - **Carbon Emitted**: 0.000 kg of CO2
410
+ - **Hours Used**: 0.003 hours
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+
412
+ ### Training Hardware
413
+ - **On Cloud**: No
414
+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
415
+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
416
+ - **RAM Size**: 31.78 GB
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+
418
+ ### Framework Versions
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+ - Python: 3.11.6
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+ - Sentence Transformers: 3.0.0.dev0
421
+ - Transformers: 4.41.0.dev0
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+ - PyTorch: 2.3.0+cu121
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+ - Accelerate: 0.26.1
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+ - Datasets: 2.18.0
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+ - Tokenizers: 0.19.1
426
+
427
+ ## Citation
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+
429
+ ### BibTeX
430
+
431
+ #### Sentence Transformers
432
+ ```bibtex
433
+ @inproceedings{reimers-2019-sentence-bert,
434
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
435
+ author = "Reimers, Nils and Gurevych, Iryna",
436
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
437
+ month = "11",
438
+ year = "2019",
439
+ publisher = "Association for Computational Linguistics",
440
+ url = "https://arxiv.org/abs/1908.10084",
441
+ }
442
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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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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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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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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+ -->
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "3.0.0.dev0",
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+ "transformers": "4.41.0.dev0",
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+ "pytorch": "2.3.0+cu121"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": null
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+ }
modules.json ADDED
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+ [
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+ {
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+ "idx": 0,
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+ "name": "0",
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+ "path": "0_WordEmbeddings",
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+ "type": "sentence_transformers.models.WordEmbeddings"
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+ },
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+ {
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+ "idx": 1,
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+ "name": "1",
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+ "path": "1_LSTM",
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+ "type": "sentence_transformers.models.LSTM"
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+ },
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+ {
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+ "idx": 2,
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+ "name": "2",
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+ "path": "2_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]