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fjmgAI
/
rerank1-210M-EuroBERT

Text Ranking
sentence-transformers
Safetensors
English
Spanish
eurobert
cross-encoder
Generated from Trainer
dataset_size:578402
loss:BinaryCrossEntropyLoss
custom_code
Eval Results
Model card Files Community
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  2. README.md +565 -0
  3. config.json +55 -0
  4. model.safetensors +3 -0
  5. special_tokens_map.json +30 -0
  6. tokenizer.json +3 -0
  7. tokenizer_config.json +2068 -0
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+ ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ tags:
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+ - sentence-transformers
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+ - cross-encoder
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+ - generated_from_trainer
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+ - dataset_size:578402
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+ - loss:BinaryCrossEntropyLoss
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+ base_model: EuroBERT/EuroBERT-210m
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+ pipeline_tag: text-ranking
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+ library_name: sentence-transformers
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+ metrics:
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+ - map
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+ - mrr@10
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+ - ndcg@10
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+ model-index:
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+ - name: EuroBERT-210m trained on GooAQ
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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: gooaq dev
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+ type: gooaq-dev
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+ metrics:
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+ - type: map
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+ value: 0.7097
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+ name: Map
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+ - type: mrr@10
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+ value: 0.7089
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+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.7579
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+ name: Ndcg@10
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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: NanoMSMARCO R100
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+ type: NanoMSMARCO_R100
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+ metrics:
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+ - type: map
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+ value: 0.463
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+ name: Map
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+ - type: mrr@10
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+ value: 0.4452
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+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.5106
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+ name: Ndcg@10
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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: NanoNFCorpus R100
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+ type: NanoNFCorpus_R100
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+ metrics:
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+ - type: map
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+ value: 0.3363
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+ name: Map
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+ - type: mrr@10
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+ value: 0.5204
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+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.3632
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+ name: Ndcg@10
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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: NanoNQ R100
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+ type: NanoNQ_R100
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+ metrics:
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+ - type: map
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+ value: 0.4738
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+ name: Map
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+ - type: mrr@10
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+ value: 0.4783
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+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.5182
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+ name: Ndcg@10
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+ - task:
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+ type: cross-encoder-nano-beir
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+ name: Cross Encoder Nano BEIR
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+ dataset:
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+ name: NanoBEIR R100 mean
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+ type: NanoBEIR_R100_mean
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+ metrics:
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+ - type: map
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+ value: 0.4244
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+ name: Map
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+ - type: mrr@10
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+ value: 0.4813
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+ name: Mrr@10
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+ - type: ndcg@10
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+ value: 0.464
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+ name: Ndcg@10
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+ ---
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+
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+ # EuroBERT-210m trained on GooAQ
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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 [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) 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.
106
+
107
+ ## Model Details
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+
109
+ ### Model Description
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+ - **Model Type:** Cross Encoder
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+ - **Base model:** [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) <!-- at revision 5a0c63d3e255a4f2005d3591d5508b7fd07cae94 -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Number of Output Labels:** 1 label
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+ <!-- - **Training Dataset:** Unknown -->
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+ - **Language:** en
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+ - **License:** apache-2.0
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+
118
+ ### Model Sources
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+
120
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
121
+ - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
122
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
123
+ - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
124
+
125
+ ## Usage
126
+
127
+ ### Direct Usage (Sentence Transformers)
128
+
129
+ First install the Sentence Transformers library:
130
+
131
+ ```bash
132
+ pip install -U sentence-transformers
133
+ ```
134
+
135
+ Then you can load this model and run inference.
136
+ ```python
137
+ from sentence_transformers import CrossEncoder
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+
139
+ # Download from the 🤗 Hub
140
+ model = CrossEncoder("cross_encoder_model_id")
141
+ # Get scores for pairs of texts
142
+ pairs = [
143
+ ['what are the risks with taking statins?', "['Muscle pain and damage. One of the most common complaints of people taking statins is muscle pain. ... ', 'Liver damage. Occasionally, statin use could cause an increase in the level of enzymes that signal liver inflammation. ... ', 'Increased blood sugar or type 2 diabetes. ... ', 'Neurological side effects.']"],
144
+ ['what are the risks with taking statins?', 'Doctors discovered that statins can help lower blood pressure, as well as lower cholesterol. Statins are often prescribed to people with high cholesterol. Too much cholesterol in your blood increases your risk of heart attacks and strokes.'],
145
+ ['what are the risks with taking statins?', 'Lipitor and Crestor are both effective statins that lower levels of “bad” cholesterol and increase levels of “good” cholesterol. While Crestor is the more potent statin, both medications are effective and have slightly different side effects and drug interactions.'],
146
+ ['what are the risks with taking statins?', "About simvastatin Simvastatin belongs to a group of medicines called statins. It's used to lower cholesterol if you've been diagnosed with high blood cholesterol. It's also taken to prevent heart disease, including heart attacks and strokes."],
147
+ ['what are the risks with taking statins?', 'Zetia works to lower cholesterol in a new way different from the statins: it inhibits the absorption of cholesterol in the small intestine, whereas the statins work by blocking cholesterol production in the liver.'],
148
+ ]
149
+ scores = model.predict(pairs)
150
+ print(scores.shape)
151
+ # (5,)
152
+
153
+ # Or rank different texts based on similarity to a single text
154
+ ranks = model.rank(
155
+ 'what are the risks with taking statins?',
156
+ [
157
+ "['Muscle pain and damage. One of the most common complaints of people taking statins is muscle pain. ... ', 'Liver damage. Occasionally, statin use could cause an increase in the level of enzymes that signal liver inflammation. ... ', 'Increased blood sugar or type 2 diabetes. ... ', 'Neurological side effects.']",
158
+ 'Doctors discovered that statins can help lower blood pressure, as well as lower cholesterol. Statins are often prescribed to people with high cholesterol. Too much cholesterol in your blood increases your risk of heart attacks and strokes.',
159
+ 'Lipitor and Crestor are both effective statins that lower levels of “bad” cholesterol and increase levels of “good” cholesterol. While Crestor is the more potent statin, both medications are effective and have slightly different side effects and drug interactions.',
160
+ "About simvastatin Simvastatin belongs to a group of medicines called statins. It's used to lower cholesterol if you've been diagnosed with high blood cholesterol. It's also taken to prevent heart disease, including heart attacks and strokes.",
161
+ 'Zetia works to lower cholesterol in a new way different from the statins: it inhibits the absorption of cholesterol in the small intestine, whereas the statins work by blocking cholesterol production in the liver.',
162
+ ]
163
+ )
164
+ # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
165
+ ```
166
+
167
+ <!--
168
+ ### Direct Usage (Transformers)
169
+
170
+ <details><summary>Click to see the direct usage in Transformers</summary>
171
+
172
+ </details>
173
+ -->
174
+
175
+ <!--
176
+ ### Downstream Usage (Sentence Transformers)
177
+
178
+ You can finetune this model on your own dataset.
179
+
180
+ <details><summary>Click to expand</summary>
181
+
182
+ </details>
183
+ -->
184
+
185
+ <!--
186
+ ### Out-of-Scope Use
187
+
188
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
189
+ -->
190
+
191
+ ## Evaluation
192
+
193
+ ### Metrics
194
+
195
+ #### Cross Encoder Reranking
196
+
197
+ * Dataset: `gooaq-dev`
198
+ * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
199
+ ```json
200
+ {
201
+ "at_k": 10,
202
+ "always_rerank_positives": false
203
+ }
204
+ ```
205
+
206
+ | Metric | Value |
207
+ |:------------|:---------------------|
208
+ | map | 0.7097 (+0.1786) |
209
+ | mrr@10 | 0.7089 (+0.1850) |
210
+ | **ndcg@10** | **0.7579 (+0.1667)** |
211
+
212
+ #### Cross Encoder Reranking
213
+
214
+ * Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
215
+ * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
216
+ ```json
217
+ {
218
+ "at_k": 10,
219
+ "always_rerank_positives": true
220
+ }
221
+ ```
222
+
223
+ | Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
224
+ |:------------|:---------------------|:---------------------|:---------------------|
225
+ | map | 0.4630 (-0.0266) | 0.3363 (+0.0753) | 0.4738 (+0.0542) |
226
+ | mrr@10 | 0.4452 (-0.0323) | 0.5204 (+0.0206) | 0.4783 (+0.0516) |
227
+ | **ndcg@10** | **0.5106 (-0.0298)** | **0.3632 (+0.0381)** | **0.5182 (+0.0176)** |
228
+
229
+ #### Cross Encoder Nano BEIR
230
+
231
+ * Dataset: `NanoBEIR_R100_mean`
232
+ * Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
233
+ ```json
234
+ {
235
+ "dataset_names": [
236
+ "msmarco",
237
+ "nfcorpus",
238
+ "nq"
239
+ ],
240
+ "rerank_k": 100,
241
+ "at_k": 10,
242
+ "always_rerank_positives": true
243
+ }
244
+ ```
245
+
246
+ | Metric | Value |
247
+ |:------------|:---------------------|
248
+ | map | 0.4244 (+0.0343) |
249
+ | mrr@10 | 0.4813 (+0.0133) |
250
+ | **ndcg@10** | **0.4640 (+0.0086)** |
251
+
252
+ <!--
253
+ ## Bias, Risks and Limitations
254
+
255
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
256
+ -->
257
+
258
+ <!--
259
+ ### Recommendations
260
+
261
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
262
+ -->
263
+
264
+ ## Training Details
265
+
266
+ ### Training Dataset
267
+
268
+ #### Unnamed Dataset
269
+
270
+ * Size: 578,402 training samples
271
+ * Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
272
+ * Approximate statistics based on the first 1000 samples:
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+ | | question | answer | label |
274
+ |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
275
+ | type | string | string | int |
276
+ | details | <ul><li>min: 20 characters</li><li>mean: 45.09 characters</li><li>max: 84 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 253.23 characters</li><li>max: 374 characters</li></ul> | <ul><li>0: ~82.60%</li><li>1: ~17.40%</li></ul> |
277
+ * Samples:
278
+ | question | answer | label |
279
+ |:-----------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
280
+ | <code>what are the risks with taking statins?</code> | <code>['Muscle pain and damage. One of the most common complaints of people taking statins is muscle pain. ... ', 'Liver damage. Occasionally, statin use could cause an increase in the level of enzymes that signal liver inflammation. ... ', 'Increased blood sugar or type 2 diabetes. ... ', 'Neurological side effects.']</code> | <code>1</code> |
281
+ | <code>what are the risks with taking statins?</code> | <code>Doctors discovered that statins can help lower blood pressure, as well as lower cholesterol. Statins are often prescribed to people with high cholesterol. Too much cholesterol in your blood increases your risk of heart attacks and strokes.</code> | <code>0</code> |
282
+ | <code>what are the risks with taking statins?</code> | <code>Lipitor and Crestor are both effective statins that lower levels of “bad” cholesterol and increase levels of “good” cholesterol. While Crestor is the more potent statin, both medications are effective and have slightly different side effects and drug interactions.</code> | <code>0</code> |
283
+ * Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
284
+ ```json
285
+ {
286
+ "activation_fn": "torch.nn.modules.linear.Identity",
287
+ "pos_weight": 5
288
+ }
289
+ ```
290
+
291
+ ### Training Hyperparameters
292
+ #### Non-Default Hyperparameters
293
+
294
+ - `eval_strategy`: steps
295
+ - `per_device_train_batch_size`: 64
296
+ - `per_device_eval_batch_size`: 64
297
+ - `learning_rate`: 2e-05
298
+ - `num_train_epochs`: 2
299
+ - `warmup_ratio`: 0.1
300
+ - `seed`: 12
301
+ - `fp16`: True
302
+ - `dataloader_num_workers`: 4
303
+ - `load_best_model_at_end`: True
304
+
305
+ #### All Hyperparameters
306
+ <details><summary>Click to expand</summary>
307
+
308
+ - `overwrite_output_dir`: False
309
+ - `do_predict`: False
310
+ - `eval_strategy`: steps
311
+ - `prediction_loss_only`: True
312
+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `per_gpu_train_batch_size`: None
315
+ - `per_gpu_eval_batch_size`: None
316
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
318
+ - `torch_empty_cache_steps`: None
319
+ - `learning_rate`: 2e-05
320
+ - `weight_decay`: 0.0
321
+ - `adam_beta1`: 0.9
322
+ - `adam_beta2`: 0.999
323
+ - `adam_epsilon`: 1e-08
324
+ - `max_grad_norm`: 1.0
325
+ - `num_train_epochs`: 2
326
+ - `max_steps`: -1
327
+ - `lr_scheduler_type`: linear
328
+ - `lr_scheduler_kwargs`: {}
329
+ - `warmup_ratio`: 0.1
330
+ - `warmup_steps`: 0
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+ - `log_level`: passive
332
+ - `log_level_replica`: warning
333
+ - `log_on_each_node`: True
334
+ - `logging_nan_inf_filter`: True
335
+ - `save_safetensors`: True
336
+ - `save_on_each_node`: False
337
+ - `save_only_model`: False
338
+ - `restore_callback_states_from_checkpoint`: False
339
+ - `no_cuda`: False
340
+ - `use_cpu`: False
341
+ - `use_mps_device`: False
342
+ - `seed`: 12
343
+ - `data_seed`: None
344
+ - `jit_mode_eval`: False
345
+ - `use_ipex`: False
346
+ - `bf16`: False
347
+ - `fp16`: True
348
+ - `fp16_opt_level`: O1
349
+ - `half_precision_backend`: auto
350
+ - `bf16_full_eval`: False
351
+ - `fp16_full_eval`: False
352
+ - `tf32`: None
353
+ - `local_rank`: 0
354
+ - `ddp_backend`: None
355
+ - `tpu_num_cores`: None
356
+ - `tpu_metrics_debug`: False
357
+ - `debug`: []
358
+ - `dataloader_drop_last`: False
359
+ - `dataloader_num_workers`: 4
360
+ - `dataloader_prefetch_factor`: None
361
+ - `past_index`: -1
362
+ - `disable_tqdm`: False
363
+ - `remove_unused_columns`: True
364
+ - `label_names`: None
365
+ - `load_best_model_at_end`: True
366
+ - `ignore_data_skip`: False
367
+ - `fsdp`: []
368
+ - `fsdp_min_num_params`: 0
369
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
370
+ - `tp_size`: 0
371
+ - `fsdp_transformer_layer_cls_to_wrap`: None
372
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
373
+ - `deepspeed`: None
374
+ - `label_smoothing_factor`: 0.0
375
+ - `optim`: adamw_torch
376
+ - `optim_args`: None
377
+ - `adafactor`: False
378
+ - `group_by_length`: False
379
+ - `length_column_name`: length
380
+ - `ddp_find_unused_parameters`: None
381
+ - `ddp_bucket_cap_mb`: None
382
+ - `ddp_broadcast_buffers`: False
383
+ - `dataloader_pin_memory`: True
384
+ - `dataloader_persistent_workers`: False
385
+ - `skip_memory_metrics`: True
386
+ - `use_legacy_prediction_loop`: False
387
+ - `push_to_hub`: False
388
+ - `resume_from_checkpoint`: None
389
+ - `hub_model_id`: None
390
+ - `hub_strategy`: every_save
391
+ - `hub_private_repo`: None
392
+ - `hub_always_push`: False
393
+ - `gradient_checkpointing`: False
394
+ - `gradient_checkpointing_kwargs`: None
395
+ - `include_inputs_for_metrics`: False
396
+ - `include_for_metrics`: []
397
+ - `eval_do_concat_batches`: True
398
+ - `fp16_backend`: auto
399
+ - `push_to_hub_model_id`: None
400
+ - `push_to_hub_organization`: None
401
+ - `mp_parameters`:
402
+ - `auto_find_batch_size`: False
403
+ - `full_determinism`: False
404
+ - `torchdynamo`: None
405
+ - `ray_scope`: last
406
+ - `ddp_timeout`: 1800
407
+ - `torch_compile`: False
408
+ - `torch_compile_backend`: None
409
+ - `torch_compile_mode`: None
410
+ - `include_tokens_per_second`: False
411
+ - `include_num_input_tokens_seen`: False
412
+ - `neftune_noise_alpha`: None
413
+ - `optim_target_modules`: None
414
+ - `batch_eval_metrics`: False
415
+ - `eval_on_start`: False
416
+ - `use_liger_kernel`: False
417
+ - `eval_use_gather_object`: False
418
+ - `average_tokens_across_devices`: False
419
+ - `prompts`: None
420
+ - `batch_sampler`: batch_sampler
421
+ - `multi_dataset_batch_sampler`: proportional
422
+
423
+ </details>
424
+
425
+ ### Training Logs
426
+ | Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
427
+ |:----------:|:---------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
428
+ | -1 | -1 | - | 0.1245 (-0.4667) | 0.0299 (-0.5105) | 0.2478 (-0.0773) | 0.0258 (-0.4748) | 0.1012 (-0.3542) |
429
+ | 0.0001 | 1 | 1.1115 | - | - | - | - | - |
430
+ | 0.0221 | 200 | 1.1756 | - | - | - | - | - |
431
+ | 0.0443 | 400 | 1.0297 | - | - | - | - | - |
432
+ | 0.0664 | 600 | 0.828 | - | - | - | - | - |
433
+ | 0.0885 | 800 | 0.7511 | - | - | - | - | - |
434
+ | 0.1106 | 1000 | 0.7224 | 0.6667 (+0.0754) | 0.4106 (-0.1299) | 0.2898 (-0.0353) | 0.4147 (-0.0860) | 0.3717 (-0.0837) |
435
+ | 0.1328 | 1200 | 0.7315 | - | - | - | - | - |
436
+ | 0.1549 | 1400 | 0.7403 | - | - | - | - | - |
437
+ | 0.1770 | 1600 | 0.731 | - | - | - | - | - |
438
+ | 0.1992 | 1800 | 0.7495 | - | - | - | - | - |
439
+ | 0.2213 | 2000 | 0.7227 | 0.6639 (+0.0727) | 0.4653 (-0.0751) | 0.4152 (+0.0901) | 0.4534 (-0.0472) | 0.4446 (-0.0107) |
440
+ | 0.2434 | 2200 | 0.7002 | - | - | - | - | - |
441
+ | 0.2655 | 2400 | 0.6803 | - | - | - | - | - |
442
+ | 0.2877 | 2600 | 0.6812 | - | - | - | - | - |
443
+ | 0.3098 | 2800 | 0.6788 | - | - | - | - | - |
444
+ | 0.3319 | 3000 | 0.6792 | 0.6908 (+0.0996) | 0.5115 (-0.0289) | 0.3722 (+0.0472) | 0.3895 (-0.1112) | 0.4244 (-0.0310) |
445
+ | 0.3541 | 3200 | 0.6701 | - | - | - | - | - |
446
+ | 0.3762 | 3400 | 0.6442 | - | - | - | - | - |
447
+ | 0.3983 | 3600 | 0.6488 | - | - | - | - | - |
448
+ | 0.4204 | 3800 | 0.6252 | - | - | - | - | - |
449
+ | 0.4426 | 4000 | 0.6368 | 0.6973 (+0.1061) | 0.5330 (-0.0074) | 0.4015 (+0.0765) | 0.5109 (+0.0103) | 0.4818 (+0.0264) |
450
+ | 0.4647 | 4200 | 0.6455 | - | - | - | - | - |
451
+ | 0.4868 | 4400 | 0.6164 | - | - | - | - | - |
452
+ | 0.5090 | 4600 | 0.6436 | - | - | - | - | - |
453
+ | 0.5311 | 4800 | 0.6094 | - | - | - | - | - |
454
+ | 0.5532 | 5000 | 0.6287 | 0.6933 (+0.1021) | 0.5572 (+0.0168) | 0.3381 (+0.0131) | 0.4847 (-0.0159) | 0.4600 (+0.0046) |
455
+ | 0.5753 | 5200 | 0.6131 | - | - | - | - | - |
456
+ | 0.5975 | 5400 | 0.6179 | - | - | - | - | - |
457
+ | 0.6196 | 5600 | 0.6007 | - | - | - | - | - |
458
+ | 0.6417 | 5800 | 0.5961 | - | - | - | - | - |
459
+ | 0.6639 | 6000 | 0.6156 | 0.7059 (+0.1146) | 0.5424 (+0.0020) | 0.3565 (+0.0315) | 0.5356 (+0.0349) | 0.4782 (+0.0228) |
460
+ | 0.6860 | 6200 | 0.5884 | - | - | - | - | - |
461
+ | 0.7081 | 6400 | 0.5824 | - | - | - | - | - |
462
+ | 0.7303 | 6600 | 0.5692 | - | - | - | - | - |
463
+ | 0.7524 | 6800 | 0.5979 | - | - | - | - | - |
464
+ | 0.7745 | 7000 | 0.5843 | 0.7206 (+0.1293) | 0.5639 (+0.0235) | 0.3664 (+0.0414) | 0.5308 (+0.0301) | 0.4870 (+0.0317) |
465
+ | 0.7966 | 7200 | 0.5864 | - | - | - | - | - |
466
+ | 0.8188 | 7400 | 0.5852 | - | - | - | - | - |
467
+ | 0.8409 | 7600 | 0.5826 | - | - | - | - | - |
468
+ | 0.8630 | 7800 | 0.5817 | - | - | - | - | - |
469
+ | 0.8852 | 8000 | 0.5666 | 0.7267 (+0.1355) | 0.5411 (+0.0007) | 0.3818 (+0.0567) | 0.5273 (+0.0266) | 0.4834 (+0.0280) |
470
+ | 0.9073 | 8200 | 0.5776 | - | - | - | - | - |
471
+ | 0.9294 | 8400 | 0.5667 | - | - | - | - | - |
472
+ | 0.9515 | 8600 | 0.5587 | - | - | - | - | - |
473
+ | 0.9737 | 8800 | 0.5593 | - | - | - | - | - |
474
+ | 0.9958 | 9000 | 0.5567 | 0.7298 (+0.1385) | 0.5091 (-0.0313) | 0.3617 (+0.0366) | 0.4834 (-0.0173) | 0.4514 (-0.0040) |
475
+ | 1.0179 | 9200 | 0.4478 | - | - | - | - | - |
476
+ | 1.0401 | 9400 | 0.4039 | - | - | - | - | - |
477
+ | 1.0622 | 9600 | 0.4101 | - | - | - | - | - |
478
+ | 1.0843 | 9800 | 0.3935 | - | - | - | - | - |
479
+ | 1.1064 | 10000 | 0.396 | 0.7338 (+0.1426) | 0.5604 (+0.0200) | 0.3518 (+0.0268) | 0.5027 (+0.0021) | 0.4716 (+0.0163) |
480
+ | 1.1286 | 10200 | 0.4077 | - | - | - | - | - |
481
+ | 1.1507 | 10400 | 0.4245 | - | - | - | - | - |
482
+ | 1.1728 | 10600 | 0.3953 | - | - | - | - | - |
483
+ | 1.1950 | 10800 | 0.3912 | - | - | - | - | - |
484
+ | 1.2171 | 11000 | 0.3943 | 0.7319 (+0.1407) | 0.5296 (-0.0108) | 0.3645 (+0.0395) | 0.4739 (-0.0268) | 0.4560 (+0.0006) |
485
+ | 1.2392 | 11200 | 0.4032 | - | - | - | - | - |
486
+ | 1.2613 | 11400 | 0.4063 | - | - | - | - | - |
487
+ | 1.2835 | 11600 | 0.3909 | - | - | - | - | - |
488
+ | 1.3056 | 11800 | 0.3759 | - | - | - | - | - |
489
+ | 1.3277 | 12000 | 0.3944 | 0.7440 (+0.1528) | 0.4978 (-0.0426) | 0.3138 (-0.0112) | 0.5306 (+0.0299) | 0.4474 (-0.0080) |
490
+ | 1.3499 | 12200 | 0.3721 | - | - | - | - | - |
491
+ | 1.3720 | 12400 | 0.3888 | - | - | - | - | - |
492
+ | 1.3941 | 12600 | 0.3838 | - | - | - | - | - |
493
+ | 1.4162 | 12800 | 0.4023 | - | - | - | - | - |
494
+ | 1.4384 | 13000 | 0.3831 | 0.7470 (+0.1558) | 0.5280 (-0.0124) | 0.3479 (+0.0229) | 0.5049 (+0.0043) | 0.4603 (+0.0049) |
495
+ | 1.4605 | 13200 | 0.4027 | - | - | - | - | - |
496
+ | 1.4826 | 13400 | 0.3977 | - | - | - | - | - |
497
+ | 1.5048 | 13600 | 0.384 | - | - | - | - | - |
498
+ | 1.5269 | 13800 | 0.3773 | - | - | - | - | - |
499
+ | 1.5490 | 14000 | 0.3854 | 0.7447 (+0.1534) | 0.5363 (-0.0041) | 0.3637 (+0.0386) | 0.5521 (+0.0515) | 0.4840 (+0.0287) |
500
+ | 1.5711 | 14200 | 0.3632 | - | - | - | - | - |
501
+ | 1.5933 | 14400 | 0.3902 | - | - | - | - | - |
502
+ | 1.6154 | 14600 | 0.3862 | - | - | - | - | - |
503
+ | 1.6375 | 14800 | 0.3702 | - | - | - | - | - |
504
+ | 1.6597 | 15000 | 0.374 | 0.7508 (+0.1595) | 0.4950 (-0.0455) | 0.3909 (+0.0658) | 0.5166 (+0.0159) | 0.4675 (+0.0121) |
505
+ | 1.6818 | 15200 | 0.3814 | - | - | - | - | - |
506
+ | 1.7039 | 15400 | 0.3713 | - | - | - | - | - |
507
+ | 1.7260 | 15600 | 0.3697 | - | - | - | - | - |
508
+ | 1.7482 | 15800 | 0.3538 | - | - | - | - | - |
509
+ | 1.7703 | 16000 | 0.3797 | 0.7491 (+0.1579) | 0.5119 (-0.0285) | 0.3635 (+0.0384) | 0.4958 (-0.0049) | 0.4570 (+0.0017) |
510
+ | 1.7924 | 16200 | 0.374 | - | - | - | - | - |
511
+ | 1.8146 | 16400 | 0.3874 | - | - | - | - | - |
512
+ | 1.8367 | 16600 | 0.3568 | - | - | - | - | - |
513
+ | 1.8588 | 16800 | 0.3653 | - | - | - | - | - |
514
+ | 1.8809 | 17000 | 0.3706 | 0.7552 (+0.1640) | 0.5025 (-0.0379) | 0.3662 (+0.0412) | 0.5036 (+0.0029) | 0.4574 (+0.0020) |
515
+ | 1.9031 | 17200 | 0.3636 | - | - | - | - | - |
516
+ | 1.9252 | 17400 | 0.3606 | - | - | - | - | - |
517
+ | 1.9473 | 17600 | 0.3599 | - | - | - | - | - |
518
+ | 1.9695 | 17800 | 0.3535 | - | - | - | - | - |
519
+ | **1.9916** | **18000** | **0.3553** | **0.7579 (+0.1667)** | **0.5106 (-0.0298)** | **0.3632 (+0.0381)** | **0.5182 (+0.0176)** | **0.4640 (+0.0086)** |
520
+
521
+ * The bold row denotes the saved checkpoint.
522
+
523
+ ### Framework Versions
524
+ - Python: 3.11.12
525
+ - Sentence Transformers: 4.0.2
526
+ - Transformers: 4.51.2
527
+ - PyTorch: 2.6.0+cu126
528
+ - Accelerate: 1.6.0
529
+ - Datasets: 3.5.0
530
+ - Tokenizers: 0.21.1
531
+
532
+ ## Citation
533
+
534
+ ### BibTeX
535
+
536
+ #### Sentence Transformers
537
+ ```bibtex
538
+ @inproceedings{reimers-2019-sentence-bert,
539
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
540
+ author = "Reimers, Nils and Gurevych, Iryna",
541
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
542
+ month = "11",
543
+ year = "2019",
544
+ publisher = "Association for Computational Linguistics",
545
+ url = "https://arxiv.org/abs/1908.10084",
546
+ }
547
+ ```
548
+
549
+ <!--
550
+ ## Glossary
551
+
552
+ *Clearly define terms in order to be accessible across audiences.*
553
+ -->
554
+
555
+ <!--
556
+ ## Model Card Authors
557
+
558
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
559
+ -->
560
+
561
+ <!--
562
+ ## Model Card Contact
563
+
564
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
565
+ -->
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1964
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1965
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1970
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1972
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1978
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1980
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1986
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2020
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2021
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2026
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2028
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2029
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2034
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2036
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2044
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2059
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2060
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2062
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2064
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2065
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2066
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2067
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2068
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