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CuongCao
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oe-bge-reranker-v2-m3-ft-v5

Text Ranking
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:5400
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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  1. .gitattributes +1 -0
  2. README.md +555 -0
  3. config.json +36 -0
  4. config_sentence_transformers.json +11 -0
  5. model.safetensors +3 -0
  6. modules.json +8 -0
  7. sentence_bert_config.json +10 -0
  8. tokenizer.json +3 -0
  9. tokenizer_config.json +16 -0
.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - cross-encoder
5
+ - reranker
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+ - generated_from_trainer
7
+ - dataset_size:5400
8
+ - loss:BinaryCrossEntropyLoss
9
+ base_model: BAAI/bge-reranker-v2-m3
10
+ pipeline_tag: text-ranking
11
+ library_name: sentence-transformers
12
+ metrics:
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+ - map
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+ - mrr@10
15
+ - ndcg@10
16
+ model-index:
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+ - name: CrossEncoder based on BAAI/bge-reranker-v2-m3
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+ results:
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+ - task:
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+ type: cross-encoder-reranking
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+ name: Cross Encoder Reranking
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+ dataset:
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+ name: train evaluator
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+ type: train-evaluator
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+ metrics:
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+ - type: map
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+ value: 0.9935
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+ name: Map
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+ - type: mrr@10
30
+ value: 0.9935
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+ name: Mrr@10
32
+ - type: ndcg@10
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+ value: 0.9952
34
+ name: Ndcg@10
35
+ - task:
36
+ type: cross-encoder-reranking
37
+ name: Cross Encoder Reranking
38
+ dataset:
39
+ name: test evaluator
40
+ type: test_evaluator
41
+ metrics:
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+ - type: map
43
+ value: 0.8883
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+ name: Map
45
+ - type: mrr@10
46
+ value: 0.8883
47
+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.9166
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+ name: Ndcg@10
51
+ ---
52
+
53
+ # CrossEncoder based on BAAI/bge-reranker-v2-m3
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+
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+ This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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+
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+ ## Model Details
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+
59
+ ### Model Description
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+ - **Model Type:** Cross Encoder
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+ - **Base model:** [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) <!-- at revision 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Number of Output Labels:** 1 label
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+ - **Supported Modality:** Text
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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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+
71
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
72
+ - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
73
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
74
+ - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
75
+
76
+ ### Full Model Architecture
77
+
78
+ ```
79
+ CrossEncoder(
80
+ (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
81
+ )
82
+ ```
83
+
84
+ ## Usage
85
+
86
+ ### Direct Usage (Sentence Transformers)
87
+
88
+ First install the Sentence Transformers library:
89
+
90
+ ```bash
91
+ pip install -U sentence-transformers
92
+ ```
93
+
94
+ Then you can load this model and run inference.
95
+ ```python
96
+ from sentence_transformers import CrossEncoder
97
+
98
+ # Download from the 🤗 Hub
99
+ model = CrossEncoder("cross_encoder_model_id")
100
+ # Get scores for pairs of inputs
101
+ pairs = [
102
+ ['Hi, is there a way to show an image once the personalization toggle is on?', "Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [here](https://tepo.app/products/conditional-logic) <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [guide](https://support.tepo.app/en/article/set-up-conditional-logic-1sp9tac/). If you prefer watching than reading, feel free to take a look at our tutorial video [here](https://www.youtube.com/watch?v=7voAaJ2lzkc)"],
103
+ ['Hi, is there a way to show an image once the personalization toggle is on?', "Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [here](https://support.tepo.app/en/article/change-product-images-based-on-selected-options-1831rew/) to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!"],
104
+ ['Hi, is there a way to show an image once the personalization toggle is on?', "Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [here](https://support.tepo.app/en/article/customize-style-for-app-options-using-css-26kmin/) for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!"],
105
+ ['Hi, is there a way to show an image once the personalization toggle is on?', 'Good day~ This is Robin from Tepo Support team! I hope your day has been amazing so far! To make sure I’m on the same page, when you say getting the older version of the option set, are you referring to undoing some changes after you’ve already hit save? If I’ve misunderstood anything, please don’t hesitate to correct me. I want to make sure I assist you as best as I can!'],
106
+ ['Hi, is there a way to show an image once the personalization toggle is on?', "> Hello there, > This is Hazel from Tepo Support team. I hope your day is going great! > Thanks for sharing your concern with us. Regarding the packing slip, we do have a step-by-step instruction [here](https://support.tepo.app/en/article/how-to-show-options-in-shopify-packing-slip-1dyay7y/) that you can follow more easily on your end. Feel free to give it a try on your end to show Tepo options in your packing slip In Shopify, a courier slip is also known as a packing slip. I've seen that Hazel has sent you the instruction to add the code into the packing slip template to show the TEPO options already. May I ask if you give it a try on your end yet?"],
107
+ ]
108
+ scores = model.predict(pairs)
109
+ print(scores)
110
+ # [9.4035e-01 1.8822e-03 2.3369e-05 2.3369e-05 2.2474e-05]
111
+
112
+ # Or rank different texts based on similarity to a single text
113
+ ranks = model.rank(
114
+ 'Hi, is there a way to show an image once the personalization toggle is on?',
115
+ [
116
+ "Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [here](https://tepo.app/products/conditional-logic) <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [guide](https://support.tepo.app/en/article/set-up-conditional-logic-1sp9tac/). If you prefer watching than reading, feel free to take a look at our tutorial video [here](https://www.youtube.com/watch?v=7voAaJ2lzkc)",
117
+ "Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [here](https://support.tepo.app/en/article/change-product-images-based-on-selected-options-1831rew/) to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!",
118
+ "Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [here](https://support.tepo.app/en/article/customize-style-for-app-options-using-css-26kmin/) for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!",
119
+ 'Good day~ This is Robin from Tepo Support team! I hope your day has been amazing so far! To make sure I’m on the same page, when you say getting the older version of the option set, are you referring to undoing some changes after you’ve already hit save? If I’ve misunderstood anything, please don’t hesitate to correct me. I want to make sure I assist you as best as I can!',
120
+ "> Hello there, > This is Hazel from Tepo Support team. I hope your day is going great! > Thanks for sharing your concern with us. Regarding the packing slip, we do have a step-by-step instruction [here](https://support.tepo.app/en/article/how-to-show-options-in-shopify-packing-slip-1dyay7y/) that you can follow more easily on your end. Feel free to give it a try on your end to show Tepo options in your packing slip In Shopify, a courier slip is also known as a packing slip. I've seen that Hazel has sent you the instruction to add the code into the packing slip template to show the TEPO options already. May I ask if you give it a try on your end yet?",
121
+ ]
122
+ )
123
+ # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
124
+ ```
125
+
126
+ <!--
127
+ ### Direct Usage (Transformers)
128
+
129
+ <details><summary>Click to see the direct usage in Transformers</summary>
130
+
131
+ </details>
132
+ -->
133
+
134
+ <!--
135
+ ### Downstream Usage (Sentence Transformers)
136
+
137
+ You can finetune this model on your own dataset.
138
+
139
+ <details><summary>Click to expand</summary>
140
+
141
+ </details>
142
+ -->
143
+
144
+ <!--
145
+ ### Out-of-Scope Use
146
+
147
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
148
+ -->
149
+
150
+ ## Evaluation
151
+
152
+ ### Metrics
153
+
154
+ #### Cross Encoder Reranking
155
+
156
+ * Datasets: `train-evaluator` and `test_evaluator`
157
+ * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
158
+ ```json
159
+ {
160
+ "at_k": 10,
161
+ "always_rerank_positives": false
162
+ }
163
+ ```
164
+
165
+ | Metric | train-evaluator | test_evaluator |
166
+ |:------------|:---------------------|:---------------------|
167
+ | map | 0.9935 (-0.0065) | 0.8883 (-0.1117) |
168
+ | mrr@10 | 0.9935 (-0.0065) | 0.8883 (-0.1117) |
169
+ | **ndcg@10** | **0.9952 (-0.0048)** | **0.9166 (-0.0834)** |
170
+
171
+ <!--
172
+ ## Bias, Risks and Limitations
173
+
174
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
175
+ -->
176
+
177
+ <!--
178
+ ### Recommendations
179
+
180
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
181
+ -->
182
+
183
+ ## Training Details
184
+
185
+ ### Training Dataset
186
+
187
+ #### Unnamed Dataset
188
+
189
+ * Size: 5,400 training samples
190
+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
191
+ * Approximate statistics based on the first 1000 samples:
192
+ | | sentence1 | sentence2 | label |
193
+ |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
194
+ | type | string | string | float |
195
+ | details | <ul><li>min: 9 tokens</li><li>mean: 55.62 tokens</li><li>max: 487 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 169.9 tokens</li><li>max: 968 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
196
+ * Samples:
197
+ | sentence1 | sentence2 | label |
198
+ |:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
199
+ | <code>Hi, is there a way to show an image once the personalization toggle is on?</code> | <code>Hi there, This is Hazel from Tepo Support team. I hope your day is going great! Yes, you can ultilize our conditional settings to show an image based on the toggle's selection, as demonstrated on our demo product [here](https://tepo.app/products/conditional-logic) <3 For step-by-step instructions on how to set up conditional settings on your end, please check out our [guide](https://support.tepo.app/en/article/set-up-conditional-logic-1sp9tac/). If you prefer watching than reading, feel free to take a look at our tutorial video [here](https://www.youtube.com/watch?v=7voAaJ2lzkc)</code> | <code>1.0</code> |
200
+ | <code>Hi, is there a way to show an image once the personalization toggle is on?</code> | <code>Hello there, I apologize for getting back to you a bit later, as we've been away during non-working hours. I hope your weekend went great! For your information, we have a feature to change the product image based on the option's selection. I have enabled this feature for your store, so feel free to follow our step-by-step instructions [here](https://support.tepo.app/en/article/change-product-images-based-on-selected-options-1831rew/) to set up this feature for your store. Please let me know if you encounter any difficulties. I am more than happy to help!</code> | <code>0.0</code> |
201
+ | <code>Hi, is there a way to show an image once the personalization toggle is on?</code> | <code>Hello, Thank you for contacting Tepo Support team! I hope you're having a wonderful day! To enlarge the size of the image swatches, you can refer to our guide [here](https://support.tepo.app/en/article/customize-style-for-app-options-using-css-26kmin/) for step-by-step instructions. This will allow you to make the necessary adjustments on your end. Feel free to give it a try, and if you encounter any difficulties or if coding isn't your forte, please don't hesitate to share the product URL with me. I'll be more than happy to assist further!</code> | <code>0.0</code> |
202
+ * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
203
+ ```json
204
+ {
205
+ "activation_fn": "torch.nn.modules.linear.Identity",
206
+ "pos_weight": 5
207
+ }
208
+ ```
209
+
210
+ ### Training Hyperparameters
211
+ #### Non-Default Hyperparameters
212
+
213
+ - `eval_strategy`: steps
214
+ - `per_device_train_batch_size`: 1
215
+ - `gradient_accumulation_steps`: 32
216
+ - `learning_rate`: 2e-05
217
+ - `weight_decay`: 0.2
218
+ - `max_grad_norm`: 0.5
219
+ - `num_train_epochs`: 5
220
+ - `warmup_steps`: 0.25
221
+ - `seed`: 67
222
+ - `fp16`: True
223
+ - `load_best_model_at_end`: True
224
+
225
+ #### All Hyperparameters
226
+ <details><summary>Click to expand</summary>
227
+
228
+ - `do_predict`: False
229
+ - `eval_strategy`: steps
230
+ - `prediction_loss_only`: True
231
+ - `per_device_train_batch_size`: 1
232
+ - `per_device_eval_batch_size`: 8
233
+ - `gradient_accumulation_steps`: 32
234
+ - `eval_accumulation_steps`: None
235
+ - `torch_empty_cache_steps`: None
236
+ - `learning_rate`: 2e-05
237
+ - `weight_decay`: 0.2
238
+ - `adam_beta1`: 0.9
239
+ - `adam_beta2`: 0.999
240
+ - `adam_epsilon`: 1e-08
241
+ - `max_grad_norm`: 0.5
242
+ - `num_train_epochs`: 5
243
+ - `max_steps`: -1
244
+ - `lr_scheduler_type`: linear
245
+ - `lr_scheduler_kwargs`: None
246
+ - `warmup_ratio`: None
247
+ - `warmup_steps`: 0.25
248
+ - `log_level`: passive
249
+ - `log_level_replica`: warning
250
+ - `log_on_each_node`: True
251
+ - `logging_nan_inf_filter`: True
252
+ - `enable_jit_checkpoint`: False
253
+ - `save_on_each_node`: False
254
+ - `save_only_model`: False
255
+ - `restore_callback_states_from_checkpoint`: False
256
+ - `use_cpu`: False
257
+ - `seed`: 67
258
+ - `data_seed`: None
259
+ - `bf16`: False
260
+ - `fp16`: True
261
+ - `bf16_full_eval`: False
262
+ - `fp16_full_eval`: False
263
+ - `tf32`: None
264
+ - `local_rank`: -1
265
+ - `ddp_backend`: None
266
+ - `debug`: []
267
+ - `dataloader_drop_last`: False
268
+ - `dataloader_num_workers`: 0
269
+ - `dataloader_prefetch_factor`: None
270
+ - `disable_tqdm`: True
271
+ - `remove_unused_columns`: True
272
+ - `label_names`: None
273
+ - `load_best_model_at_end`: True
274
+ - `ignore_data_skip`: False
275
+ - `fsdp`: []
276
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
277
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
278
+ - `parallelism_config`: None
279
+ - `deepspeed`: None
280
+ - `label_smoothing_factor`: 0.0
281
+ - `optim`: adamw_torch_fused
282
+ - `optim_args`: None
283
+ - `group_by_length`: False
284
+ - `length_column_name`: length
285
+ - `project`: huggingface
286
+ - `trackio_space_id`: trackio
287
+ - `ddp_find_unused_parameters`: None
288
+ - `ddp_bucket_cap_mb`: None
289
+ - `ddp_broadcast_buffers`: False
290
+ - `dataloader_pin_memory`: True
291
+ - `dataloader_persistent_workers`: False
292
+ - `skip_memory_metrics`: True
293
+ - `push_to_hub`: False
294
+ - `resume_from_checkpoint`: None
295
+ - `hub_model_id`: None
296
+ - `hub_strategy`: every_save
297
+ - `hub_private_repo`: None
298
+ - `hub_always_push`: False
299
+ - `hub_revision`: None
300
+ - `gradient_checkpointing`: False
301
+ - `gradient_checkpointing_kwargs`: None
302
+ - `include_for_metrics`: []
303
+ - `eval_do_concat_batches`: True
304
+ - `auto_find_batch_size`: False
305
+ - `full_determinism`: False
306
+ - `ddp_timeout`: 1800
307
+ - `torch_compile`: False
308
+ - `torch_compile_backend`: None
309
+ - `torch_compile_mode`: None
310
+ - `include_num_input_tokens_seen`: no
311
+ - `neftune_noise_alpha`: None
312
+ - `optim_target_modules`: None
313
+ - `batch_eval_metrics`: False
314
+ - `eval_on_start`: False
315
+ - `use_liger_kernel`: False
316
+ - `liger_kernel_config`: None
317
+ - `eval_use_gather_object`: False
318
+ - `average_tokens_across_devices`: True
319
+ - `use_cache`: False
320
+ - `prompts`: None
321
+ - `batch_sampler`: batch_sampler
322
+ - `multi_dataset_batch_sampler`: proportional
323
+ - `router_mapping`: {}
324
+ - `learning_rate_mapping`: {}
325
+
326
+ </details>
327
+
328
+ ### Training Logs
329
+ <details><summary>Click to expand</summary>
330
+
331
+ | Epoch | Step | Training Loss | train-evaluator_ndcg@10 | test_evaluator_ndcg@10 |
332
+ |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|
333
+ | -1 | -1 | - | 0.8634 (-0.1366) | 0.8746 (-0.1254) |
334
+ | 0.0059 | 1 | 1.4427 | - | - |
335
+ | 0.0296 | 5 | 1.0900 | - | - |
336
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+ | 0.1778 | 30 | 0.7740 | - | - |
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+ | 0.2074 | 35 | 1.1410 | - | - |
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+ | 0.2667 | 45 | 0.7899 | - | - |
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+ | 0.3259 | 55 | 0.7696 | - | - |
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+ | 0.3556 | 60 | 0.9953 | - | - |
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+ | 0.3852 | 65 | 0.7479 | - | - |
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+ | 0.4148 | 70 | 0.6350 | - | - |
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+ | 0.4444 | 75 | 0.8330 | - | - |
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+ | 0.4741 | 80 | 0.6033 | - | - |
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+ | 0.5037 | 85 | 0.8338 | - | - |
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+ | 0.5333 | 90 | 0.9694 | - | - |
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+ | 0.5630 | 95 | 0.9039 | - | - |
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+ | 0.5926 | 100 | 0.8650 | 0.9112 (-0.0888) | 0.8927 (-0.1073) |
355
+ | 0.6222 | 105 | 0.6094 | - | - |
356
+ | 0.6519 | 110 | 0.6067 | - | - |
357
+ | 0.6815 | 115 | 0.5759 | - | - |
358
+ | 0.7111 | 120 | 0.8194 | - | - |
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+ | 0.7407 | 125 | 0.7709 | - | - |
360
+ | 0.7704 | 130 | 0.7898 | - | - |
361
+ | 0.8 | 135 | 0.8466 | - | - |
362
+ | 0.8296 | 140 | 0.9746 | - | - |
363
+ | 0.8593 | 145 | 0.8148 | - | - |
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+ | 0.8889 | 150 | 0.5513 | 0.9312 (-0.0688) | 0.9096 (-0.0904) |
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+ | 0.9185 | 155 | 0.5055 | - | - |
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+ | 0.9481 | 160 | 0.5476 | - | - |
367
+ | 0.9778 | 165 | 0.7248 | - | - |
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+ | 1.0059 | 170 | 0.6541 | - | - |
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+ | 1.0356 | 175 | 0.5039 | - | - |
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+ | 1.0652 | 180 | 0.4411 | - | - |
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+ | 1.0948 | 185 | 0.6942 | - | - |
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+ | 1.1244 | 190 | 0.5658 | - | - |
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+ | 1.2133 | 205 | 0.6112 | - | - |
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+ | 1.2430 | 210 | 0.2897 | - | - |
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+ | 1.3319 | 225 | 0.7286 | - | - |
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472
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473
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477
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486
+ | 4.4978 | 760 | 0.0901 | - | - |
487
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488
+ | 4.5570 | 770 | 0.1037 | - | - |
489
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491
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492
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500
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501
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+ | 5.0 | 845 | 0.1853 | - | - |
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+
505
+ * The bold row denotes the saved checkpoint.
506
+ </details>
507
+
508
+ ### Training Time
509
+ - **Training**: 1.5 hours
510
+ - **Evaluation**: 23.9 minutes
511
+ - **Total**: 1.9 hours
512
+
513
+ ### Framework Versions
514
+ - Python: 3.12.13
515
+ - Sentence Transformers: 5.4.0
516
+ - Transformers: 5.0.0
517
+ - PyTorch: 2.10.0+cu128
518
+ - Accelerate: 1.13.0
519
+ - Datasets: 4.0.0
520
+ - Tokenizers: 0.22.2
521
+
522
+ ## Citation
523
+
524
+ ### BibTeX
525
+
526
+ #### Sentence Transformers
527
+ ```bibtex
528
+ @inproceedings{reimers-2019-sentence-bert,
529
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
530
+ author = "Reimers, Nils and Gurevych, Iryna",
531
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
532
+ month = "11",
533
+ year = "2019",
534
+ publisher = "Association for Computational Linguistics",
535
+ url = "https://arxiv.org/abs/1908.10084",
536
+ }
537
+ ```
538
+
539
+ <!--
540
+ ## Glossary
541
+
542
+ *Clearly define terms in order to be accessible across audiences.*
543
+ -->
544
+
545
+ <!--
546
+ ## Model Card Authors
547
+
548
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
549
+ -->
550
+
551
+ <!--
552
+ ## Model Card Contact
553
+
554
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
555
+ -->
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