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  1. .gitattributes +1 -0
  2. final/1_Pooling/config.json +10 -0
  3. final/README.md +524 -0
  4. final/config.json +28 -0
  5. final/config_sentence_transformers.json +10 -0
  6. final/model-00001-of-00002.safetensors +3 -0
  7. final/model-00002-of-00002.safetensors +3 -0
  8. final/model.safetensors.index.json +590 -0
  9. final/modules.json +14 -0
  10. final/sentence_bert_config.json +4 -0
  11. final/sentencepiece.bpe.model +3 -0
  12. final/special_tokens_map.json +51 -0
  13. final/tokenizer.json +3 -0
  14. final/tokenizer_config.json +55 -0
  15. runs/Jun03_19-22-55_ruche-gpu19.cluster/events.out.tfevents.1717435434.ruche-gpu19.cluster.26229.0 +3 -0
  16. runs/Jun03_19-29-06_ruche-gpu19.cluster/events.out.tfevents.1717435801.ruche-gpu19.cluster.27463.0 +3 -0
  17. runs/Jun03_19-34-46_ruche-gpu19.cluster/events.out.tfevents.1717436145.ruche-gpu19.cluster.28125.0 +3 -0
  18. runs/Jun03_19-42-14_ruche-gpu19.cluster/events.out.tfevents.1717436574.ruche-gpu19.cluster.29230.0 +3 -0
.gitattributes CHANGED
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final/1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 2560,
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+ "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,
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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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+ }
final/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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+ - dataset_size:100K<n<1M
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+ - loss:CachedMultipleNegativesRankingLoss
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+ base_model: facebook/xlm-roberta-xl
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+ metrics:
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+ - cosine_accuracy
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+ - dot_accuracy
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+ - manhattan_accuracy
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+ - euclidean_accuracy
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+ - max_accuracy
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+ widget:
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+ - source_sentence: A bird flying.
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+ sentences:
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+ - A butterfly flys freely.
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+ - the animal is running
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+ - The woman is asleep.
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+ - source_sentence: There's a dock
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+ sentences:
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+ - The boat is sinking
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+ - A man is ice skating.
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+ - A man is sleeping.
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+ - source_sentence: A woman sings.
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+ sentences:
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+ - The woman is singing.
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+ - The girl is standing.
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+ - A boy is sledding
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+ - source_sentence: The boy scowls
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+ sentences:
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+ - The boy is smiling
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+ - a child eats cheerios
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+ - A woman is sitting.
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+ - source_sentence: a baby smiling
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+ sentences:
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+ - The boy is smiling
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+ - The woman is fishing.
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+ - A man is sitting down.
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+ pipeline_tag: sentence-similarity
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+ model-index:
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+ - name: SentenceTransformer based on facebook/xlm-roberta-xl
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+ results:
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: all nli dev
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+ type: all-nli-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.931
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.063
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.931
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.927
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.931
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+ name: Max Accuracy
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: all nli test
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+ type: all-nli-test
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.939
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.056
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.936
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.938
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.939
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+ name: Max Accuracy
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+ ---
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+
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+ # SentenceTransformer based on facebook/xlm-roberta-xl
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 2560-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
97
+
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+ ## Model Details
99
+
100
+ ### Model Description
101
+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) <!-- at revision aa5d120255845efeebc9b7f42822a1dd0f9ece9d -->
103
+ - **Maximum Sequence Length:** 514 tokens
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+ - **Output Dimensionality:** 2560 tokens
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+ - **Similarity Function:** Cosine Similarity
106
+ - **Training Dataset:**
107
+ - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
108
+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
112
+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
114
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
115
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
117
+ ### Full Model Architecture
118
+
119
+ ```
120
+ SentenceTransformer(
121
+ (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: XLMRobertaXLModel
122
+ (1): Pooling({'word_embedding_dimension': 2560, '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})
123
+ )
124
+ ```
125
+
126
+ ## Usage
127
+
128
+ ### Direct Usage (Sentence Transformers)
129
+
130
+ First install the Sentence Transformers library:
131
+
132
+ ```bash
133
+ pip install -U sentence-transformers
134
+ ```
135
+
136
+ Then you can load this model and run inference.
137
+ ```python
138
+ from sentence_transformers import SentenceTransformer
139
+
140
+ # Download from the 🤗 Hub
141
+ model = SentenceTransformer("sentence_transformers_model_id")
142
+ # Run inference
143
+ sentences = [
144
+ 'a baby smiling',
145
+ 'The boy is smiling',
146
+ 'The woman is fishing.',
147
+ ]
148
+ embeddings = model.encode(sentences)
149
+ print(embeddings.shape)
150
+ # [3, 2560]
151
+
152
+ # Get the similarity scores for the embeddings
153
+ similarities = model.similarity(embeddings, embeddings)
154
+ print(similarities.shape)
155
+ # [3, 3]
156
+ ```
157
+
158
+ <!--
159
+ ### Direct Usage (Transformers)
160
+
161
+ <details><summary>Click to see the direct usage in Transformers</summary>
162
+
163
+ </details>
164
+ -->
165
+
166
+ <!--
167
+ ### Downstream Usage (Sentence Transformers)
168
+
169
+ You can finetune this model on your own dataset.
170
+
171
+ <details><summary>Click to expand</summary>
172
+
173
+ </details>
174
+ -->
175
+
176
+ <!--
177
+ ### Out-of-Scope Use
178
+
179
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
180
+ -->
181
+
182
+ ## Evaluation
183
+
184
+ ### Metrics
185
+
186
+ #### Triplet
187
+ * Dataset: `all-nli-dev`
188
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
189
+
190
+ | Metric | Value |
191
+ |:-------------------|:----------|
192
+ | cosine_accuracy | 0.931 |
193
+ | dot_accuracy | 0.063 |
194
+ | manhattan_accuracy | 0.931 |
195
+ | euclidean_accuracy | 0.927 |
196
+ | **max_accuracy** | **0.931** |
197
+
198
+ #### Triplet
199
+ * Dataset: `all-nli-test`
200
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
201
+
202
+ | Metric | Value |
203
+ |:-------------------|:----------|
204
+ | cosine_accuracy | 0.939 |
205
+ | dot_accuracy | 0.056 |
206
+ | manhattan_accuracy | 0.936 |
207
+ | euclidean_accuracy | 0.938 |
208
+ | **max_accuracy** | **0.939** |
209
+
210
+ <!--
211
+ ## Bias, Risks and Limitations
212
+
213
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
214
+ -->
215
+
216
+ <!--
217
+ ### Recommendations
218
+
219
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
220
+ -->
221
+
222
+ ## Training Details
223
+
224
+ ### Training Dataset
225
+
226
+ #### sentence-transformers/all-nli
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+
228
+ * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
229
+ * Size: 100,000 training samples
230
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
231
+ * Approximate statistics based on the first 1000 samples:
232
+ | | anchor | positive | negative |
233
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
234
+ | type | string | string | string |
235
+ | details | <ul><li>min: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> |
236
+ * Samples:
237
+ | anchor | positive | negative |
238
+ |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
239
+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
240
+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
241
+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
242
+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
243
+ ```json
244
+ {
245
+ "scale": 20.0,
246
+ "similarity_fct": "cos_sim"
247
+ }
248
+ ```
249
+
250
+ ### Evaluation Dataset
251
+
252
+ #### sentence-transformers/all-nli
253
+
254
+ * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
255
+ * Size: 1,000 evaluation samples
256
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
257
+ * Approximate statistics based on the first 1000 samples:
258
+ | | anchor | positive | negative |
259
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
260
+ | type | string | string | string |
261
+ | details | <ul><li>min: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> |
262
+ * Samples:
263
+ | anchor | positive | negative |
264
+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
265
+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
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+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
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+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
268
+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
269
+ ```json
270
+ {
271
+ "scale": 20.0,
272
+ "similarity_fct": "cos_sim"
273
+ }
274
+ ```
275
+
276
+ ### Training Hyperparameters
277
+ #### Non-Default Hyperparameters
278
+
279
+ - `eval_strategy`: steps
280
+ - `per_device_train_batch_size`: 16
281
+ - `per_device_eval_batch_size`: 16
282
+ - `num_train_epochs`: 1
283
+ - `warmup_ratio`: 0.1
284
+ - `bf16`: True
285
+ - `batch_sampler`: no_duplicates
286
+
287
+ #### All Hyperparameters
288
+ <details><summary>Click to expand</summary>
289
+
290
+ - `overwrite_output_dir`: False
291
+ - `do_predict`: False
292
+ - `eval_strategy`: steps
293
+ - `prediction_loss_only`: True
294
+ - `per_device_train_batch_size`: 16
295
+ - `per_device_eval_batch_size`: 16
296
+ - `per_gpu_train_batch_size`: None
297
+ - `per_gpu_eval_batch_size`: None
298
+ - `gradient_accumulation_steps`: 1
299
+ - `eval_accumulation_steps`: None
300
+ - `learning_rate`: 5e-05
301
+ - `weight_decay`: 0.0
302
+ - `adam_beta1`: 0.9
303
+ - `adam_beta2`: 0.999
304
+ - `adam_epsilon`: 1e-08
305
+ - `max_grad_norm`: 1.0
306
+ - `num_train_epochs`: 1
307
+ - `max_steps`: -1
308
+ - `lr_scheduler_type`: linear
309
+ - `lr_scheduler_kwargs`: {}
310
+ - `warmup_ratio`: 0.1
311
+ - `warmup_steps`: 0
312
+ - `log_level`: passive
313
+ - `log_level_replica`: warning
314
+ - `log_on_each_node`: True
315
+ - `logging_nan_inf_filter`: True
316
+ - `save_safetensors`: True
317
+ - `save_on_each_node`: False
318
+ - `save_only_model`: False
319
+ - `restore_callback_states_from_checkpoint`: False
320
+ - `no_cuda`: False
321
+ - `use_cpu`: False
322
+ - `use_mps_device`: False
323
+ - `seed`: 42
324
+ - `data_seed`: None
325
+ - `jit_mode_eval`: False
326
+ - `use_ipex`: False
327
+ - `bf16`: True
328
+ - `fp16`: False
329
+ - `fp16_opt_level`: O1
330
+ - `half_precision_backend`: auto
331
+ - `bf16_full_eval`: False
332
+ - `fp16_full_eval`: False
333
+ - `tf32`: None
334
+ - `local_rank`: 0
335
+ - `ddp_backend`: None
336
+ - `tpu_num_cores`: None
337
+ - `tpu_metrics_debug`: False
338
+ - `debug`: []
339
+ - `dataloader_drop_last`: False
340
+ - `dataloader_num_workers`: 0
341
+ - `dataloader_prefetch_factor`: None
342
+ - `past_index`: -1
343
+ - `disable_tqdm`: False
344
+ - `remove_unused_columns`: True
345
+ - `label_names`: None
346
+ - `load_best_model_at_end`: False
347
+ - `ignore_data_skip`: False
348
+ - `fsdp`: []
349
+ - `fsdp_min_num_params`: 0
350
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
351
+ - `fsdp_transformer_layer_cls_to_wrap`: None
352
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
353
+ - `deepspeed`: None
354
+ - `label_smoothing_factor`: 0.0
355
+ - `optim`: adamw_torch
356
+ - `optim_args`: None
357
+ - `adafactor`: False
358
+ - `group_by_length`: False
359
+ - `length_column_name`: length
360
+ - `ddp_find_unused_parameters`: None
361
+ - `ddp_bucket_cap_mb`: None
362
+ - `ddp_broadcast_buffers`: False
363
+ - `dataloader_pin_memory`: True
364
+ - `dataloader_persistent_workers`: False
365
+ - `skip_memory_metrics`: True
366
+ - `use_legacy_prediction_loop`: False
367
+ - `push_to_hub`: False
368
+ - `resume_from_checkpoint`: None
369
+ - `hub_model_id`: None
370
+ - `hub_strategy`: every_save
371
+ - `hub_private_repo`: False
372
+ - `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
390
+ - `split_batches`: None
391
+ - `include_tokens_per_second`: False
392
+ - `include_num_input_tokens_seen`: False
393
+ - `neftune_noise_alpha`: None
394
+ - `optim_target_modules`: None
395
+ - `batch_eval_metrics`: False
396
+ - `batch_sampler`: no_duplicates
397
+ - `multi_dataset_batch_sampler`: proportional
398
+
399
+ </details>
400
+
401
+ ### Training Logs
402
+ | Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
403
+ |:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
404
+ | 0 | 0 | - | - | 0.536 | - |
405
+ | 0.016 | 100 | 3.4661 | 3.3640 | 0.54 | - |
406
+ | 0.032 | 200 | 3.4151 | 3.3148 | 0.592 | - |
407
+ | 0.048 | 300 | 3.1576 | 1.8296 | 0.82 | - |
408
+ | 0.064 | 400 | 1.2552 | 0.8366 | 0.879 | - |
409
+ | 0.08 | 500 | 0.9262 | 0.8402 | 0.888 | - |
410
+ | 0.096 | 600 | 1.0456 | 0.8762 | 0.899 | - |
411
+ | 0.112 | 700 | 0.9238 | 0.7967 | 0.894 | - |
412
+ | 0.128 | 800 | 1.0304 | 0.8797 | 0.881 | - |
413
+ | 0.144 | 900 | 1.1126 | 0.7939 | 0.903 | - |
414
+ | 0.16 | 1000 | 1.0165 | 0.7052 | 0.908 | - |
415
+ | 0.176 | 1100 | 1.06 | 0.6926 | 0.905 | - |
416
+ | 0.192 | 1200 | 0.9598 | 0.6843 | 0.894 | - |
417
+ | 0.208 | 1300 | 0.7719 | 0.6999 | 0.894 | - |
418
+ | 0.224 | 1400 | 0.7425 | 0.6932 | 0.902 | - |
419
+ | 0.24 | 1500 | 0.8434 | 0.5910 | 0.914 | - |
420
+ | 0.256 | 1600 | 0.6589 | 0.6143 | 0.903 | - |
421
+ | 0.272 | 1700 | 0.8642 | 0.6560 | 0.906 | - |
422
+ | 0.288 | 1800 | 0.838 | 0.6180 | 0.907 | - |
423
+ | 0.304 | 1900 | 0.8458 | 0.6097 | 0.91 | - |
424
+ | 0.32 | 2000 | 0.774 | 0.5675 | 0.914 | - |
425
+ | 0.336 | 2100 | 0.63 | 0.5917 | 0.909 | - |
426
+ | 0.352 | 2200 | 0.6185 | 0.5801 | 0.911 | - |
427
+ | 0.368 | 2300 | 0.8125 | 0.5623 | 0.915 | - |
428
+ | 0.384 | 2400 | 0.8478 | 0.5355 | 0.91 | - |
429
+ | 0.4 | 2500 | 0.7576 | 0.5490 | 0.91 | - |
430
+ | 0.416 | 2600 | 0.6457 | 0.5197 | 0.913 | - |
431
+ | 0.432 | 2700 | 0.918 | 0.5464 | 0.91 | - |
432
+ | 0.448 | 2800 | 0.8934 | 0.5375 | 0.91 | - |
433
+ | 0.464 | 2900 | 0.8407 | 0.5175 | 0.915 | - |
434
+ | 0.48 | 3000 | 0.7036 | 0.5054 | 0.919 | - |
435
+ | 0.496 | 3100 | 0.7929 | 0.5101 | 0.912 | - |
436
+ | 0.512 | 3200 | 0.613 | 0.4948 | 0.914 | - |
437
+ | 0.528 | 3300 | 0.5894 | 0.4953 | 0.915 | - |
438
+ | 0.544 | 3400 | 0.5885 | 0.4931 | 0.915 | - |
439
+ | 0.56 | 3500 | 0.6328 | 0.4868 | 0.921 | - |
440
+ | 0.576 | 3600 | 0.6128 | 0.4768 | 0.919 | - |
441
+ | 0.592 | 3700 | 0.5823 | 0.4730 | 0.922 | - |
442
+ | 0.608 | 3800 | 0.5633 | 0.4638 | 0.922 | - |
443
+ | 0.624 | 3900 | 0.5729 | 0.4587 | 0.926 | - |
444
+ | 0.64 | 4000 | 0.7778 | 0.4590 | 0.925 | - |
445
+ | 0.656 | 4100 | 1.0064 | 0.4550 | 0.925 | - |
446
+ | 0.672 | 4200 | 0.9683 | 0.4505 | 0.927 | - |
447
+ | 0.688 | 4300 | 0.7752 | 0.4470 | 0.926 | - |
448
+ | 0.704 | 4400 | 0.6738 | 0.4421 | 0.927 | - |
449
+ | 0.72 | 4500 | 0.6862 | 0.4374 | 0.928 | - |
450
+ | 0.736 | 4600 | 0.7699 | 0.4361 | 0.93 | - |
451
+ | 0.752 | 4700 | 0.72 | 0.4329 | 0.931 | - |
452
+ | 0.768 | 4800 | 0.7304 | 0.4306 | 0.929 | - |
453
+ | 0.784 | 4900 | 0.6679 | 0.4284 | 0.93 | - |
454
+ | 0.8 | 5000 | 0.7516 | 0.4285 | 0.93 | - |
455
+ | 0.816 | 5100 | 0.6286 | 0.4266 | 0.931 | - |
456
+ | 0.832 | 5200 | 0.7631 | 0.4274 | 0.931 | - |
457
+ | 0.848 | 5300 | 0.6368 | 0.4270 | 0.932 | - |
458
+ | 0.864 | 5400 | 0.767 | 0.4268 | 0.932 | - |
459
+ | 0.88 | 5500 | 0.6655 | 0.4254 | 0.933 | - |
460
+ | 0.896 | 5600 | 0.6599 | 0.4249 | 0.931 | - |
461
+ | 0.912 | 5700 | 0.722 | 0.4250 | 0.933 | - |
462
+ | 0.928 | 5800 | 0.682 | 0.4247 | 0.931 | - |
463
+ | 0.944 | 5900 | 0.7892 | 0.4245 | 0.931 | - |
464
+ | 0.96 | 6000 | 0.8095 | 0.4245 | 0.931 | - |
465
+ | 0.976 | 6100 | 0.6783 | 0.4243 | 0.932 | - |
466
+ | 0.992 | 6200 | 0.0051 | 0.4246 | 0.931 | - |
467
+ | 1.0 | 6250 | - | - | - | 0.939 |
468
+
469
+
470
+ ### Framework Versions
471
+ - Python: 3.9.10
472
+ - Sentence Transformers: 3.0.0
473
+ - Transformers: 4.41.2
474
+ - PyTorch: 2.3.0+cu121
475
+ - Accelerate: 0.26.1
476
+ - Datasets: 2.16.1
477
+ - Tokenizers: 0.19.1
478
+
479
+ ## Citation
480
+
481
+ ### BibTeX
482
+
483
+ #### Sentence Transformers
484
+ ```bibtex
485
+ @inproceedings{reimers-2019-sentence-bert,
486
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
487
+ author = "Reimers, Nils and Gurevych, Iryna",
488
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
489
+ month = "11",
490
+ year = "2019",
491
+ publisher = "Association for Computational Linguistics",
492
+ url = "https://arxiv.org/abs/1908.10084",
493
+ }
494
+ ```
495
+
496
+ #### CachedMultipleNegativesRankingLoss
497
+ ```bibtex
498
+ @misc{gao2021scaling,
499
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
500
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
501
+ year={2021},
502
+ eprint={2101.06983},
503
+ archivePrefix={arXiv},
504
+ primaryClass={cs.LG}
505
+ }
506
+ ```
507
+
508
+ <!--
509
+ ## Glossary
510
+
511
+ *Clearly define terms in order to be accessible across audiences.*
512
+ -->
513
+
514
+ <!--
515
+ ## Model Card Authors
516
+
517
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
518
+ -->
519
+
520
+ <!--
521
+ ## Model Card Contact
522
+
523
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
524
+ -->
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