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hilabs
/
automapper_molina

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:91097
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +667 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +14 -0
  5. label_dict.json +267 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. molina_data_package.zip +3 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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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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+ license: apache-2.0
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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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+ - dense
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+ - generated_from_trainer
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+ - dataset_size:91097
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: NeuML/pubmedbert-base-embeddings
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+ widget:
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+ - source_sentence: '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>provider gen suffix<VALUE>Jr,
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+ Sr, II, III||III||III||M||II||Jr ||III||Jr.||Sr. '
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+ sentences:
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Gen suffix
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-OBSERVATION
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>State License
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+ - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Trust-Based Relational Intervention
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+ (TBRI)<VALUE>FALSE
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+ sentences:
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-CHILD PSYCHOLOGY
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician NPI
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Trust-Based Relational Intervention (TBRI)
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+ - source_sentence: '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>action<VALUE>Now has AMS Medicare_x000D_
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+
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+ Effective Date 10/7/2023_x000D_
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+
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+ TIN 74-1613878||Effective 11/7/2023 - Previously credentialed; please add to Baylor
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+ contract and fee schedules under TIN 74-1613878 and link to all applicable Baylor
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+ products/networks. Close Panel and Directory Suppress as they are Hospital Based.||Now
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+ Enrolled in AMS Medicaid_x000D_
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+
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+ Effective Date 8/1/2023 _x000D_
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+
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+ TIN 74-1613878'
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+ sentences:
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-OBSERVATION
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Autism/Applied Behavioral Analysis (ABA)
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Change Request Type
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+ - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>language_written_indicator<VALUE>YES||NO
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+ sentences:
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Medicare Number
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Language Written?
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Education Specialty
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+ - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT> affiliate address<VALUE>200 E
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+ ARIZONA||3480 E GUASTI RD
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+ sentences:
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital State
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Addr Line 1
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+ - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>State License
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
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+ model-index:
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+ - name: txt_std_ra_automapper_molina
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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: txt std ra automapper molina
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+ type: txt_std_ra_automapper_molina
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.9426305294036865
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+ name: Cosine Accuracy
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+ ---
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+
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+ # txt_std_ra_automapper_molina
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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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:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 dimensions
84
+ - **Similarity Function:** Cosine Similarity
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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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+
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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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+
95
+ ### Full Model Architecture
96
+
97
+ ```
98
+ SentenceTransformer(
99
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
100
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
101
+ )
102
+ ```
103
+
104
+ ## Usage
105
+
106
+ ### Direct Usage (Sentence Transformers)
107
+
108
+ First install the Sentence Transformers library:
109
+
110
+ ```bash
111
+ pip install -U sentence-transformers
112
+ ```
113
+
114
+ Then you can load this model and run inference.
115
+ ```python
116
+ from sentence_transformers import SentenceTransformer
117
+
118
+ # Download from the 🤗 Hub
119
+ model = SentenceTransformer("sentence_transformers_model_id")
120
+ # Run inference
121
+ sentences = [
122
+ '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT> affiliate address<VALUE>200 E ARIZONA||3480 E GUASTI RD',
123
+ '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Addr Line 1',
124
+ '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital State',
125
+ ]
126
+ embeddings = model.encode(sentences)
127
+ print(embeddings.shape)
128
+ # [3, 768]
129
+
130
+ # Get the similarity scores for the embeddings
131
+ similarities = model.similarity(embeddings, embeddings)
132
+ print(similarities)
133
+ # tensor([[1.0000, 0.9630, 0.0580],
134
+ # [0.9630, 1.0000, 0.0549],
135
+ # [0.0580, 0.0549, 1.0000]])
136
+ ```
137
+
138
+ <!--
139
+ ### Direct Usage (Transformers)
140
+
141
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
143
+ </details>
144
+ -->
145
+
146
+ <!--
147
+ ### Downstream Usage (Sentence Transformers)
148
+
149
+ You can finetune this model on your own dataset.
150
+
151
+ <details><summary>Click to expand</summary>
152
+
153
+ </details>
154
+ -->
155
+
156
+ <!--
157
+ ### Out-of-Scope Use
158
+
159
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
160
+ -->
161
+
162
+ ## Evaluation
163
+
164
+ ### Metrics
165
+
166
+ #### Triplet
167
+
168
+ * Dataset: `txt_std_ra_automapper_molina`
169
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
170
+
171
+ | Metric | Value |
172
+ |:--------------------|:-----------|
173
+ | **cosine_accuracy** | **0.9426** |
174
+
175
+ <!--
176
+ ## Bias, Risks and Limitations
177
+
178
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
179
+ -->
180
+
181
+ <!--
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+ ### Recommendations
183
+
184
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
185
+ -->
186
+
187
+ ## Training Details
188
+
189
+ ### Training Dataset
190
+
191
+ #### Unnamed Dataset
192
+
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+ * Size: 91,097 training samples
194
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
195
+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
197
+ |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
198
+ | type | string | string | string |
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+ | details | <ul><li>min: 21 tokens</li><li>mean: 45.98 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.65 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.86 tokens</li><li>max: 30 tokens</li></ul> |
200
+ * Samples:
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+ | anchor | positive | negative |
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+ |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
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+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>license from (01/01/1901)<VALUE>03-11-2021</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>License effective date</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Provider Term Reason</code> |
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+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>taxid<VALUE>994566602\|\|954558726\|\|431857688\|\|06-0873021\|\|611313932\|\|202530877</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>TIN</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Min Age</code> |
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+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>pri tax id number<VALUE>920379978</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>TIN</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Intellectual Disability Development</code> |
206
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
207
+ ```json
208
+ {
209
+ "scale": 20.0,
210
+ "similarity_fct": "cos_sim",
211
+ "gather_across_devices": false
212
+ }
213
+ ```
214
+
215
+ ### Evaluation Dataset
216
+
217
+ #### Unnamed Dataset
218
+
219
+ * Size: 9,709 evaluation samples
220
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
221
+ * Approximate statistics based on the first 1000 samples:
222
+ | | anchor | positive | negative |
223
+ |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
224
+ | type | string | string | string |
225
+ | details | <ul><li>min: 21 tokens</li><li>mean: 46.15 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.7 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.94 tokens</li><li>max: 30 tokens</li></ul> |
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+ * Samples:
227
+ | anchor | positive | negative |
228
+ |:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
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+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>provider term date (01/01/1901)<VALUE>11-02-2027</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Provider term Date</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Network Directory Suppression Reason Code</code> |
230
+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Address Line 2<VALUE>Ste 300Tvw\|\|SUITE 1A\|\|#206\|\|490 Route 304\|\|Suite 210</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Address Line 2</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Term Date</code> |
231
+ | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>education1_specialty<VALUE>Infectious Disease\|\|Pediatrics</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Education Specialty</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician NPI</code> |
232
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
233
+ ```json
234
+ {
235
+ "scale": 20.0,
236
+ "similarity_fct": "cos_sim",
237
+ "gather_across_devices": false
238
+ }
239
+ ```
240
+
241
+ ### Training Hyperparameters
242
+ #### Non-Default Hyperparameters
243
+
244
+ - `eval_strategy`: steps
245
+ - `per_device_train_batch_size`: 16
246
+ - `per_device_eval_batch_size`: 16
247
+ - `num_train_epochs`: 2
248
+ - `warmup_ratio`: 0.1
249
+ - `fp16`: True
250
+ - `load_best_model_at_end`: True
251
+ - `batch_sampler`: no_duplicates
252
+
253
+ #### All Hyperparameters
254
+ <details><summary>Click to expand</summary>
255
+
256
+ - `overwrite_output_dir`: False
257
+ - `do_predict`: False
258
+ - `eval_strategy`: steps
259
+ - `prediction_loss_only`: True
260
+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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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
266
+ - `torch_empty_cache_steps`: None
267
+ - `learning_rate`: 5e-05
268
+ - `weight_decay`: 0.0
269
+ - `adam_beta1`: 0.9
270
+ - `adam_beta2`: 0.999
271
+ - `adam_epsilon`: 1e-08
272
+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 2
274
+ - `max_steps`: -1
275
+ - `lr_scheduler_type`: linear
276
+ - `lr_scheduler_kwargs`: {}
277
+ - `warmup_ratio`: 0.1
278
+ - `warmup_steps`: 0
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+ - `log_level`: passive
280
+ - `log_level_replica`: warning
281
+ - `log_on_each_node`: True
282
+ - `logging_nan_inf_filter`: True
283
+ - `save_safetensors`: True
284
+ - `save_on_each_node`: False
285
+ - `save_only_model`: False
286
+ - `restore_callback_states_from_checkpoint`: False
287
+ - `no_cuda`: False
288
+ - `use_cpu`: False
289
+ - `use_mps_device`: False
290
+ - `seed`: 42
291
+ - `data_seed`: None
292
+ - `jit_mode_eval`: False
293
+ - `use_ipex`: False
294
+ - `bf16`: False
295
+ - `fp16`: True
296
+ - `fp16_opt_level`: O1
297
+ - `half_precision_backend`: auto
298
+ - `bf16_full_eval`: False
299
+ - `fp16_full_eval`: False
300
+ - `tf32`: None
301
+ - `local_rank`: 0
302
+ - `ddp_backend`: None
303
+ - `tpu_num_cores`: None
304
+ - `tpu_metrics_debug`: False
305
+ - `debug`: []
306
+ - `dataloader_drop_last`: False
307
+ - `dataloader_num_workers`: 0
308
+ - `dataloader_prefetch_factor`: None
309
+ - `past_index`: -1
310
+ - `disable_tqdm`: False
311
+ - `remove_unused_columns`: True
312
+ - `label_names`: None
313
+ - `load_best_model_at_end`: True
314
+ - `ignore_data_skip`: False
315
+ - `fsdp`: []
316
+ - `fsdp_min_num_params`: 0
317
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
318
+ - `fsdp_transformer_layer_cls_to_wrap`: None
319
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
320
+ - `deepspeed`: None
321
+ - `label_smoothing_factor`: 0.0
322
+ - `optim`: adamw_torch_fused
323
+ - `optim_args`: None
324
+ - `adafactor`: False
325
+ - `group_by_length`: False
326
+ - `length_column_name`: length
327
+ - `ddp_find_unused_parameters`: None
328
+ - `ddp_bucket_cap_mb`: None
329
+ - `ddp_broadcast_buffers`: False
330
+ - `dataloader_pin_memory`: True
331
+ - `dataloader_persistent_workers`: False
332
+ - `skip_memory_metrics`: True
333
+ - `use_legacy_prediction_loop`: False
334
+ - `push_to_hub`: False
335
+ - `resume_from_checkpoint`: None
336
+ - `hub_model_id`: None
337
+ - `hub_strategy`: every_save
338
+ - `hub_private_repo`: None
339
+ - `hub_always_push`: False
340
+ - `hub_revision`: None
341
+ - `gradient_checkpointing`: False
342
+ - `gradient_checkpointing_kwargs`: None
343
+ - `include_inputs_for_metrics`: False
344
+ - `include_for_metrics`: []
345
+ - `eval_do_concat_batches`: True
346
+ - `fp16_backend`: auto
347
+ - `push_to_hub_model_id`: None
348
+ - `push_to_hub_organization`: None
349
+ - `mp_parameters`:
350
+ - `auto_find_batch_size`: False
351
+ - `full_determinism`: False
352
+ - `torchdynamo`: None
353
+ - `ray_scope`: last
354
+ - `ddp_timeout`: 1800
355
+ - `torch_compile`: False
356
+ - `torch_compile_backend`: None
357
+ - `torch_compile_mode`: None
358
+ - `include_tokens_per_second`: False
359
+ - `include_num_input_tokens_seen`: False
360
+ - `neftune_noise_alpha`: None
361
+ - `optim_target_modules`: None
362
+ - `batch_eval_metrics`: False
363
+ - `eval_on_start`: False
364
+ - `use_liger_kernel`: False
365
+ - `liger_kernel_config`: None
366
+ - `eval_use_gather_object`: False
367
+ - `average_tokens_across_devices`: False
368
+ - `prompts`: None
369
+ - `batch_sampler`: no_duplicates
370
+ - `multi_dataset_batch_sampler`: proportional
371
+ - `router_mapping`: {}
372
+ - `learning_rate_mapping`: {}
373
+
374
+ </details>
375
+
376
+ ### Training Logs
377
+ <details><summary>Click to expand</summary>
378
+
379
+ | Epoch | Step | Training Loss | Validation Loss | txt_std_ra_automapper_molina_cosine_accuracy |
380
+ |:---------:|:--------:|:-------------:|:---------------:|:--------------------------------------------:|
381
+ | -1 | -1 | - | - | 0.9426 |
382
+ | 0.0088 | 50 | 1.7127 | - | - |
383
+ | 0.0176 | 100 | 0.8476 | - | - |
384
+ | 0.0263 | 150 | 0.5572 | - | - |
385
+ | 0.0351 | 200 | 0.4306 | 0.2925 | - |
386
+ | 0.0439 | 250 | 0.3464 | - | - |
387
+ | 0.0527 | 300 | 0.2764 | - | - |
388
+ | 0.0615 | 350 | 0.2381 | - | - |
389
+ | 0.0702 | 400 | 0.1622 | 0.1312 | - |
390
+ | 0.0790 | 450 | 0.1617 | - | - |
391
+ | 0.0878 | 500 | 0.1492 | - | - |
392
+ | 0.0966 | 550 | 0.1429 | - | - |
393
+ | 0.1054 | 600 | 0.114 | 0.0835 | - |
394
+ | 0.1142 | 650 | 0.1203 | - | - |
395
+ | 0.1229 | 700 | 0.0901 | - | - |
396
+ | 0.1317 | 750 | 0.1014 | - | - |
397
+ | 0.1405 | 800 | 0.0796 | 0.0614 | - |
398
+ | 0.1493 | 850 | 0.0631 | - | - |
399
+ | 0.1581 | 900 | 0.0989 | - | - |
400
+ | 0.1668 | 950 | 0.0627 | - | - |
401
+ | 0.1756 | 1000 | 0.0809 | 0.0670 | - |
402
+ | 0.1844 | 1050 | 0.0638 | - | - |
403
+ | 0.1932 | 1100 | 0.0664 | - | - |
404
+ | 0.2020 | 1150 | 0.0419 | - | - |
405
+ | 0.2107 | 1200 | 0.0569 | 0.0506 | - |
406
+ | 0.2195 | 1250 | 0.0842 | - | - |
407
+ | 0.2283 | 1300 | 0.0557 | - | - |
408
+ | 0.2371 | 1350 | 0.0653 | - | - |
409
+ | 0.2459 | 1400 | 0.065 | 0.0438 | - |
410
+ | 0.2547 | 1450 | 0.0459 | - | - |
411
+ | 0.2634 | 1500 | 0.0644 | - | - |
412
+ | 0.2722 | 1550 | 0.0494 | - | - |
413
+ | 0.2810 | 1600 | 0.0532 | 0.0296 | - |
414
+ | 0.2898 | 1650 | 0.0792 | - | - |
415
+ | 0.2986 | 1700 | 0.0592 | - | - |
416
+ | 0.3073 | 1750 | 0.0503 | - | - |
417
+ | 0.3161 | 1800 | 0.0353 | 0.0375 | - |
418
+ | 0.3249 | 1850 | 0.0556 | - | - |
419
+ | 0.3337 | 1900 | 0.0545 | - | - |
420
+ | 0.3425 | 1950 | 0.035 | - | - |
421
+ | 0.3512 | 2000 | 0.0286 | 0.0219 | - |
422
+ | 0.3600 | 2050 | 0.0305 | - | - |
423
+ | 0.3688 | 2100 | 0.014 | - | - |
424
+ | 0.3776 | 2150 | 0.0307 | - | - |
425
+ | 0.3864 | 2200 | 0.0374 | 0.0242 | - |
426
+ | 0.3952 | 2250 | 0.039 | - | - |
427
+ | 0.4039 | 2300 | 0.0218 | - | - |
428
+ | 0.4127 | 2350 | 0.0416 | - | - |
429
+ | 0.4215 | 2400 | 0.038 | 0.0186 | - |
430
+ | 0.4303 | 2450 | 0.0282 | - | - |
431
+ | 0.4391 | 2500 | 0.0171 | - | - |
432
+ | 0.4478 | 2550 | 0.0282 | - | - |
433
+ | 0.4566 | 2600 | 0.0227 | 0.0185 | - |
434
+ | 0.4654 | 2650 | 0.02 | - | - |
435
+ | 0.4742 | 2700 | 0.0215 | - | - |
436
+ | 0.4830 | 2750 | 0.0328 | - | - |
437
+ | 0.4917 | 2800 | 0.0118 | 0.0165 | - |
438
+ | 0.5005 | 2850 | 0.0278 | - | - |
439
+ | 0.5093 | 2900 | 0.0072 | - | - |
440
+ | 0.5181 | 2950 | 0.0252 | - | - |
441
+ | 0.5269 | 3000 | 0.0162 | 0.0151 | - |
442
+ | 0.5357 | 3050 | 0.0241 | - | - |
443
+ | 0.5444 | 3100 | 0.0042 | - | - |
444
+ | 0.5532 | 3150 | 0.0157 | - | - |
445
+ | 0.5620 | 3200 | 0.0256 | 0.0141 | - |
446
+ | 0.5708 | 3250 | 0.0106 | - | - |
447
+ | 0.5796 | 3300 | 0.0138 | - | - |
448
+ | 0.5883 | 3350 | 0.0292 | - | - |
449
+ | 0.5971 | 3400 | 0.0133 | 0.0164 | - |
450
+ | 0.6059 | 3450 | 0.0105 | - | - |
451
+ | 0.6147 | 3500 | 0.0148 | - | - |
452
+ | 0.6235 | 3550 | 0.0101 | - | - |
453
+ | 0.6322 | 3600 | 0.0101 | 0.0136 | - |
454
+ | 0.6410 | 3650 | 0.0271 | - | - |
455
+ | 0.6498 | 3700 | 0.028 | - | - |
456
+ | 0.6586 | 3750 | 0.0057 | - | - |
457
+ | 0.6674 | 3800 | 0.0273 | 0.0101 | - |
458
+ | 0.6762 | 3850 | 0.0201 | - | - |
459
+ | 0.6849 | 3900 | 0.0164 | - | - |
460
+ | 0.6937 | 3950 | 0.0425 | - | - |
461
+ | 0.7025 | 4000 | 0.0168 | 0.0112 | - |
462
+ | 0.7113 | 4050 | 0.0174 | - | - |
463
+ | 0.7201 | 4100 | 0.0153 | - | - |
464
+ | 0.7288 | 4150 | 0.0166 | - | - |
465
+ | 0.7376 | 4200 | 0.0252 | 0.0078 | - |
466
+ | 0.7464 | 4250 | 0.0098 | - | - |
467
+ | 0.7552 | 4300 | 0.0145 | - | - |
468
+ | 0.7640 | 4350 | 0.0141 | - | - |
469
+ | 0.7727 | 4400 | 0.0119 | 0.0088 | - |
470
+ | 0.7815 | 4450 | 0.0108 | - | - |
471
+ | 0.7903 | 4500 | 0.0146 | - | - |
472
+ | 0.7991 | 4550 | 0.0104 | - | - |
473
+ | 0.8079 | 4600 | 0.0068 | 0.0116 | - |
474
+ | 0.8166 | 4650 | 0.0233 | - | - |
475
+ | 0.8254 | 4700 | 0.0028 | - | - |
476
+ | 0.8342 | 4750 | 0.0255 | - | - |
477
+ | 0.8430 | 4800 | 0.009 | 0.0127 | - |
478
+ | 0.8518 | 4850 | 0.0293 | - | - |
479
+ | 0.8606 | 4900 | 0.0045 | - | - |
480
+ | 0.8693 | 4950 | 0.0048 | - | - |
481
+ | 0.8781 | 5000 | 0.0178 | 0.0132 | - |
482
+ | 0.8869 | 5050 | 0.0059 | - | - |
483
+ | 0.8957 | 5100 | 0.0221 | - | - |
484
+ | 0.9045 | 5150 | 0.0082 | - | - |
485
+ | 0.9132 | 5200 | 0.0111 | 0.0097 | - |
486
+ | 0.9220 | 5250 | 0.0021 | - | - |
487
+ | 0.9308 | 5300 | 0.0034 | - | - |
488
+ | 0.9396 | 5350 | 0.0449 | - | - |
489
+ | 0.9484 | 5400 | 0.0128 | 0.0066 | - |
490
+ | 0.9571 | 5450 | 0.0095 | - | - |
491
+ | 0.9659 | 5500 | 0.009 | - | - |
492
+ | 0.9747 | 5550 | 0.0169 | - | - |
493
+ | 0.9835 | 5600 | 0.0115 | 0.0060 | - |
494
+ | 0.9923 | 5650 | 0.0204 | - | - |
495
+ | 1.0011 | 5700 | 0.0116 | - | - |
496
+ | 1.0098 | 5750 | 0.0049 | - | - |
497
+ | 1.0186 | 5800 | 0.0064 | 0.0096 | - |
498
+ | 1.0274 | 5850 | 0.0061 | - | - |
499
+ | 1.0362 | 5900 | 0.0011 | - | - |
500
+ | 1.0450 | 5950 | 0.018 | - | - |
501
+ | 1.0537 | 6000 | 0.0231 | 0.0056 | - |
502
+ | 1.0625 | 6050 | 0.0081 | - | - |
503
+ | 1.0713 | 6100 | 0.0021 | - | - |
504
+ | 1.0801 | 6150 | 0.006 | - | - |
505
+ | 1.0889 | 6200 | 0.0116 | 0.0078 | - |
506
+ | 1.0976 | 6250 | 0.0074 | - | - |
507
+ | 1.1064 | 6300 | 0.0082 | - | - |
508
+ | 1.1152 | 6350 | 0.0011 | - | - |
509
+ | 1.1240 | 6400 | 0.0051 | 0.0101 | - |
510
+ | 1.1328 | 6450 | 0.007 | - | - |
511
+ | 1.1416 | 6500 | 0.0015 | - | - |
512
+ | 1.1503 | 6550 | 0.0037 | - | - |
513
+ | 1.1591 | 6600 | 0.0027 | 0.0073 | - |
514
+ | 1.1679 | 6650 | 0.0005 | - | - |
515
+ | 1.1767 | 6700 | 0.0239 | - | - |
516
+ | 1.1855 | 6750 | 0.0136 | - | - |
517
+ | 1.1942 | 6800 | 0.0251 | 0.0070 | - |
518
+ | 1.2030 | 6850 | 0.0004 | - | - |
519
+ | 1.2118 | 6900 | 0.0065 | - | - |
520
+ | 1.2206 | 6950 | 0.0109 | - | - |
521
+ | 1.2294 | 7000 | 0.0009 | 0.0043 | - |
522
+ | 1.2381 | 7050 | 0.0086 | - | - |
523
+ | 1.2469 | 7100 | 0.003 | - | - |
524
+ | 1.2557 | 7150 | 0.0044 | - | - |
525
+ | 1.2645 | 7200 | 0.0118 | 0.0058 | - |
526
+ | 1.2733 | 7250 | 0.0093 | - | - |
527
+ | 1.2821 | 7300 | 0.0023 | - | - |
528
+ | 1.2908 | 7350 | 0.002 | - | - |
529
+ | 1.2996 | 7400 | 0.007 | 0.0061 | - |
530
+ | 1.3084 | 7450 | 0.0162 | - | - |
531
+ | 1.3172 | 7500 | 0.0011 | - | - |
532
+ | 1.3260 | 7550 | 0.007 | - | - |
533
+ | 1.3347 | 7600 | 0.0014 | 0.0048 | - |
534
+ | 1.3435 | 7650 | 0.0033 | - | - |
535
+ | 1.3523 | 7700 | 0.0007 | - | - |
536
+ | 1.3611 | 7750 | 0.0017 | - | - |
537
+ | 1.3699 | 7800 | 0.0078 | 0.0049 | - |
538
+ | 1.3786 | 7850 | 0.0049 | - | - |
539
+ | 1.3874 | 7900 | 0.003 | - | - |
540
+ | 1.3962 | 7950 | 0.0028 | - | - |
541
+ | 1.4050 | 8000 | 0.0038 | 0.0033 | - |
542
+ | 1.4138 | 8050 | 0.0158 | - | - |
543
+ | 1.4226 | 8100 | 0.0008 | - | - |
544
+ | 1.4313 | 8150 | 0.0007 | - | - |
545
+ | 1.4401 | 8200 | 0.0038 | 0.0024 | - |
546
+ | 1.4489 | 8250 | 0.0177 | - | - |
547
+ | 1.4577 | 8300 | 0.0044 | - | - |
548
+ | 1.4665 | 8350 | 0.0064 | - | - |
549
+ | 1.4752 | 8400 | 0.0005 | 0.0049 | - |
550
+ | 1.4840 | 8450 | 0.0146 | - | - |
551
+ | 1.4928 | 8500 | 0.001 | - | - |
552
+ | 1.5016 | 8550 | 0.0014 | - | - |
553
+ | 1.5104 | 8600 | 0.0041 | 0.0038 | - |
554
+ | 1.5191 | 8650 | 0.0072 | - | - |
555
+ | 1.5279 | 8700 | 0.0014 | - | - |
556
+ | 1.5367 | 8750 | 0.0135 | - | - |
557
+ | 1.5455 | 8800 | 0.0148 | 0.0039 | - |
558
+ | 1.5543 | 8850 | 0.0017 | - | - |
559
+ | 1.5630 | 8900 | 0.007 | - | - |
560
+ | 1.5718 | 8950 | 0.012 | - | - |
561
+ | 1.5806 | 9000 | 0.0004 | 0.0024 | - |
562
+ | 1.5894 | 9050 | 0.0026 | - | - |
563
+ | 1.5982 | 9100 | 0.0109 | - | - |
564
+ | 1.6070 | 9150 | 0.0009 | - | - |
565
+ | 1.6157 | 9200 | 0.0054 | 0.0022 | - |
566
+ | 1.6245 | 9250 | 0.0032 | - | - |
567
+ | 1.6333 | 9300 | 0.0135 | - | - |
568
+ | 1.6421 | 9350 | 0.0131 | - | - |
569
+ | 1.6509 | 9400 | 0.0049 | 0.0021 | - |
570
+ | 1.6596 | 9450 | 0.0003 | - | - |
571
+ | 1.6684 | 9500 | 0.0027 | - | - |
572
+ | 1.6772 | 9550 | 0.0008 | - | - |
573
+ | **1.686** | **9600** | **0.0124** | **0.002** | **-** |
574
+ | 1.6948 | 9650 | 0.0026 | - | - |
575
+ | 1.7035 | 9700 | 0.004 | - | - |
576
+ | 1.7123 | 9750 | 0.0008 | - | - |
577
+ | 1.7211 | 9800 | 0.0058 | 0.0028 | - |
578
+ | 1.7299 | 9850 | 0.0133 | - | - |
579
+ | 1.7387 | 9900 | 0.0005 | - | - |
580
+ | 1.7475 | 9950 | 0.0007 | - | - |
581
+ | 1.7562 | 10000 | 0.0009 | 0.0040 | - |
582
+ | 1.7650 | 10050 | 0.0018 | - | - |
583
+ | 1.7738 | 10100 | 0.0002 | - | - |
584
+ | 1.7826 | 10150 | 0.002 | - | - |
585
+ | 1.7914 | 10200 | 0.0021 | 0.0042 | - |
586
+ | 1.8001 | 10250 | 0.002 | - | - |
587
+ | 1.8089 | 10300 | 0.0003 | - | - |
588
+ | 1.8177 | 10350 | 0.001 | - | - |
589
+ | 1.8265 | 10400 | 0.0092 | 0.0045 | - |
590
+ | 1.8353 | 10450 | 0.0032 | - | - |
591
+ | 1.8440 | 10500 | 0.0002 | - | - |
592
+ | 1.8528 | 10550 | 0.0026 | - | - |
593
+ | 1.8616 | 10600 | 0.0003 | 0.0048 | - |
594
+ | 1.8704 | 10650 | 0.001 | - | - |
595
+ | 1.8792 | 10700 | 0.0126 | - | - |
596
+ | 1.8880 | 10750 | 0.0172 | - | - |
597
+ | 1.8967 | 10800 | 0.0002 | 0.0034 | - |
598
+ | 1.9055 | 10850 | 0.0038 | - | - |
599
+ | 1.9143 | 10900 | 0.0005 | - | - |
600
+ | 1.9231 | 10950 | 0.0001 | - | - |
601
+ | 1.9319 | 11000 | 0.0162 | 0.0033 | - |
602
+ | 1.9406 | 11050 | 0.0037 | - | - |
603
+ | 1.9494 | 11100 | 0.0003 | - | - |
604
+ | 1.9582 | 11150 | 0.0021 | - | - |
605
+ | 1.9670 | 11200 | 0.0098 | 0.0032 | - |
606
+ | 1.9758 | 11250 | 0.0003 | - | - |
607
+ | 1.9845 | 11300 | 0.0017 | - | - |
608
+ | 1.9933 | 11350 | 0.0048 | - | - |
609
+
610
+ * The bold row denotes the saved checkpoint.
611
+ </details>
612
+
613
+ ### Framework Versions
614
+ - Python: 3.10.17
615
+ - Sentence Transformers: 5.1.0
616
+ - Transformers: 4.55.0
617
+ - PyTorch: 2.8.0+cu128
618
+ - Accelerate: 1.7.0
619
+ - Datasets: 3.5.0
620
+ - Tokenizers: 0.21.4
621
+
622
+ ## Citation
623
+
624
+ ### BibTeX
625
+
626
+ #### Sentence Transformers
627
+ ```bibtex
628
+ @inproceedings{reimers-2019-sentence-bert,
629
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
630
+ author = "Reimers, Nils and Gurevych, Iryna",
631
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
632
+ month = "11",
633
+ year = "2019",
634
+ publisher = "Association for Computational Linguistics",
635
+ url = "https://arxiv.org/abs/1908.10084",
636
+ }
637
+ ```
638
+
639
+ #### MultipleNegativesRankingLoss
640
+ ```bibtex
641
+ @misc{henderson2017efficient,
642
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
643
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
644
+ year={2017},
645
+ eprint={1705.00652},
646
+ archivePrefix={arXiv},
647
+ primaryClass={cs.CL}
648
+ }
649
+ ```
650
+
651
+ <!--
652
+ ## Glossary
653
+
654
+ *Clearly define terms in order to be accessible across audiences.*
655
+ -->
656
+
657
+ <!--
658
+ ## Model Card Authors
659
+
660
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
661
+ -->
662
+
663
+ <!--
664
+ ## Model Card Contact
665
+
666
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
667
+ -->
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "BertModel"
4
+ ],
5
+ "attention_probs_dropout_prob": 0.1,
6
+ "classifier_dropout": null,
7
+ "hidden_act": "gelu",
8
+ "hidden_dropout_prob": 0.1,
9
+ "hidden_size": 768,
10
+ "initializer_range": 0.02,
11
+ "intermediate_size": 3072,
12
+ "layer_norm_eps": 1e-12,
13
+ "max_position_embeddings": 512,
14
+ "model_type": "bert",
15
+ "num_attention_heads": 12,
16
+ "num_hidden_layers": 12,
17
+ "pad_token_id": 0,
18
+ "position_embedding_type": "absolute",
19
+ "torch_dtype": "float32",
20
+ "transformers_version": "4.55.0",
21
+ "type_vocab_size": 2,
22
+ "use_cache": true,
23
+ "vocab_size": 30522
24
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "5.1.0",
4
+ "transformers": "4.55.0",
5
+ "pytorch": "2.8.0+cu128"
6
+ },
7
+ "model_type": "SentenceTransformer",
8
+ "prompts": {
9
+ "query": "",
10
+ "document": ""
11
+ },
12
+ "default_prompt_name": null,
13
+ "similarity_fn_name": "cosine"
14
+ }
label_dict.json ADDED
@@ -0,0 +1,267 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Certification Effective Date": 0,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Specialty Term Date": 1,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Remit Fax": 2,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Designated Childrens Hospital": 3,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Last Recredentialing Date": 4,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Primary Phone Number": 5,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Address Term Reason": 6,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Education Type": 7,
10
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Email Term Date": 8,
11
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Addr Line 1": 9,
12
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Language Spoken?": 10,
13
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Addr Line 2": 11,
14
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>CAQH State": 12,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Address Term Date": 13,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Child Parent Psychotherapy (CPP)": 14,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Sunday Office Hours": 15,
18
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>State License": 16,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Address Line 1": 17,
20
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital State": 18,
21
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital-based? Y or N": 19,
22
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Institution Issuing License": 20,
23
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Medicaid State": 21,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Office Services": 22,
25
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Corr State": 23,
26
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-METHADONE PROVIDER": 24,
27
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Remittance Name": 25,
28
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Physical Disabilities": 26,
29
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>IPA TIN": 27,
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+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Provider Language": 28,
31
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Middle Name": 29,
32
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Alt ID Effective Date": 30,
33
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Affiliation Name": 31,
34
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Gen suffix": 32,
35
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Certification Board": 33,
36
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Address Directory Suppress": 34,
37
+ "<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>County": 35,
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+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "additional_special_tokens": [],
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "[CLS]",
47
+ "do_basic_tokenize": true,
48
+ "do_lower_case": true,
49
+ "extra_special_tokens": {},
50
+ "mask_token": "[MASK]",
51
+ "max_length": 512,
52
+ "model_max_length": 512,
53
+ "never_split": null,
54
+ "pad_to_multiple_of": null,
55
+ "pad_token": "[PAD]",
56
+ "pad_token_type_id": 0,
57
+ "padding_side": "right",
58
+ "sep_token": "[SEP]",
59
+ "stride": 0,
60
+ "strip_accents": null,
61
+ "tokenize_chinese_chars": true,
62
+ "tokenizer_class": "BertTokenizer",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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