Hugging Face's logo

cbpuschmann
/
MiniLM-klimacoder_v0.5

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
cbpuschmann commited on
Commit
7c9a1c8
•
1 Parent(s): e952965

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +720 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +8 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 384,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,720 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - setfit
4
+ - sentence-transformers
5
+ - text-classification
6
+ - generated_from_setfit_trainer
7
+ widget:
8
+ - text: ' "Ein Tempolimit auf deutschen Autobahnen wäre ein Schlag ins Gesicht aller
9
+ Autofahrer, die Freiheit und Unabhängigkeit schätzen."'
10
+ - text: Die Bundesregierung prüft derzeit mehrere Gesetzesinitiativen, die ein generelles
11
+ Tempolimit auf deutschen Autobahnen vorsehen.
12
+ - text: ' Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!'
13
+ - text: '"Während sich unsere Politiker auf ihren Klimakonferenzen über die Notwendigkeit
14
+ neuer Heizungssysteme unterhalten, vergessen sie dabei geflissentlich, dass die
15
+ einfache Frau Schmidt oder der einfache Herr Müller bald jeden zweiten Lohnscheck
16
+ direkt in die Kasse des Heizungsexperten oder des Energiekonzerns überweisen werden."'
17
+ - text: ' "Das geplante Heizungsgesetz ist ein weiterer Schritt in Richtung staatlicher
18
+ Bevormundung und wird die Bürger in die Armut treiben."'
19
+ metrics:
20
+ - accuracy
21
+ pipeline_tag: text-classification
22
+ library_name: setfit
23
+ inference: true
24
+ base_model: sentence-transformers/all-MiniLM-L6-v2
25
+ model-index:
26
+ - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
27
+ results:
28
+ - task:
29
+ type: text-classification
30
+ name: Text Classification
31
+ dataset:
32
+ name: Unknown
33
+ type: unknown
34
+ split: test
35
+ metrics:
36
+ - type: accuracy
37
+ value: 0.931899641577061
38
+ name: Accuracy
39
+ ---
40
+
41
+ # SetFit with sentence-transformers/all-MiniLM-L6-v2
42
+
43
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
44
+
45
+ The model has been trained using an efficient few-shot learning technique that involves:
46
+
47
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
48
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
49
+
50
+ ## Model Details
51
+
52
+ ### Model Description
53
+ - **Model Type:** SetFit
54
+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
55
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
56
+ - **Maximum Sequence Length:** 256 tokens
57
+ - **Number of Classes:** 3 classes
58
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
59
+ <!-- - **Language:** Unknown -->
60
+ <!-- - **License:** Unknown -->
61
+
62
+ ### Model Sources
63
+
64
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
65
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
66
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
67
+
68
+ ### Model Labels
69
+ | Label | Examples |
70
+ |:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
71
+ | neutral | <ul><li>' Die Aktionen von Klima-Aktivismus-Gruppen wie Fridays for Future oder die Letzte Generation polarisieren die Öffentlichkeit, während sie gleichzeitig wichtige Diskussionen über den Klimawandel anstoßen.'</li><li>'Die Diskussion um ein generelles Tempolimit auf Autobahnen hat in den vergangenen Wochen an Fahrt gewonnen und sowohl Befürworter als auch Gegner haben ihre Positionen deutlich gemacht.'</li><li>' "Das geplante Heizungsgesetz sieht vor, dass ab 2024 in Neubauten und bei der Sanierung von Bestandsgebäuden verstärkt auf Wärmepumpen gesetzt werden soll."'</li></ul> |
72
+ | supportive | <ul><li>'Die Einführung eines generellen Tempolimits auf deutschen Autobahnen würde nicht nur zu einer Senkung des Kraftstoffverbrauchs und der Treibhausgasemissionen führen, sondern auch die Verkehrssicherheit erhöhen.'</li><li>' "Ein nationales Tempolimit auf Autobahnen könnte laut Experten die Verkehrssicherheit erheblich verbessern und gleichzeitig den CO2-Ausstoß reduzieren."'</li><li>' "Das geplante Heizungsgesetz könnte einen wichtigen Beitrag zur Reduzierung von CO2-Emissionen leisten und somit einen bedeutenden Schritt in Richtung Klimaneutralität darstellen."'</li></ul> |
73
+ | opposed | <ul><li>'Die Freiheit der Straße, ein Stück deutscher Identität, das in Gefahr geraten könnte, wenn die politischen Tempolimit-Fanatiker ihren Willen durchsetzen.'</li><li>' "Es reicht! Wann hören diese Klima-Aktivisten endlich auf, unsere Straßen zu blockieren und den Alltag der hart arbeitenden Bürger zu stören?"'</li><li>'„Die Blockaden von Straßen und Autobahnen durch die Letzte Generation sorgen für tägliche Nervosität bei Pendler und Anwohner, die sich fragen, wann diese ständigen Behinderungen endlich ein Ende haben werden.“'</li></ul> |
74
+
75
+ ## Evaluation
76
+
77
+ ### Metrics
78
+ | Label | Accuracy |
79
+ |:--------|:---------|
80
+ | **all** | 0.9319 |
81
+
82
+ ## Uses
83
+
84
+ ### Direct Use for Inference
85
+
86
+ First install the SetFit library:
87
+
88
+ ```bash
89
+ pip install setfit
90
+ ```
91
+
92
+ Then you can load this model and run inference.
93
+
94
+ ```python
95
+ from setfit import SetFitModel
96
+
97
+ # Download from the 🤗 Hub
98
+ model = SetFitModel.from_pretrained("cbpuschmann/MiniLM-klimacoder_v0.5")
99
+ # Run inference
100
+ preds = model(" Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!")
101
+ ```
102
+
103
+ <!--
104
+ ### Downstream Use
105
+
106
+ *List how someone could finetune this model on their own dataset.*
107
+ -->
108
+
109
+ <!--
110
+ ### Out-of-Scope Use
111
+
112
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
113
+ -->
114
+
115
+ <!--
116
+ ## Bias, Risks and Limitations
117
+
118
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
119
+ -->
120
+
121
+ <!--
122
+ ### Recommendations
123
+
124
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
125
+ -->
126
+
127
+ ## Training Details
128
+
129
+ ### Training Set Metrics
130
+ | Training set | Min | Median | Max |
131
+ |:-------------|:----|:--------|:----|
132
+ | Word count | 11 | 25.5421 | 57 |
133
+
134
+ | Label | Training Sample Count |
135
+ |:-----------|:----------------------|
136
+ | neutral | 326 |
137
+ | opposed | 394 |
138
+ | supportive | 396 |
139
+
140
+ ### Training Hyperparameters
141
+ - batch_size: (32, 32)
142
+ - num_epochs: (1, 1)
143
+ - max_steps: -1
144
+ - sampling_strategy: oversampling
145
+ - body_learning_rate: (2e-05, 1e-05)
146
+ - head_learning_rate: 0.01
147
+ - loss: CosineSimilarityLoss
148
+ - distance_metric: cosine_distance
149
+ - margin: 0.25
150
+ - end_to_end: False
151
+ - use_amp: False
152
+ - warmup_proportion: 0.1
153
+ - l2_weight: 0.01
154
+ - seed: 42
155
+ - eval_max_steps: -1
156
+ - load_best_model_at_end: False
157
+
158
+ ### Training Results
159
+ | Epoch | Step | Training Loss | Validation Loss |
160
+ |:------:|:-----:|:-------------:|:---------------:|
161
+ | 0.0000 | 1 | 0.2393 | - |
162
+ | 0.0019 | 50 | 0.2748 | - |
163
+ | 0.0039 | 100 | 0.2607 | - |
164
+ | 0.0058 | 150 | 0.2486 | - |
165
+ | 0.0077 | 200 | 0.2465 | - |
166
+ | 0.0097 | 250 | 0.246 | - |
167
+ | 0.0116 | 300 | 0.2454 | - |
168
+ | 0.0135 | 350 | 0.2406 | - |
169
+ | 0.0155 | 400 | 0.235 | - |
170
+ | 0.0174 | 450 | 0.2269 | - |
171
+ | 0.0193 | 500 | 0.2184 | - |
172
+ | 0.0213 | 550 | 0.2095 | - |
173
+ | 0.0232 | 600 | 0.1833 | - |
174
+ | 0.0251 | 650 | 0.1777 | - |
175
+ | 0.0271 | 700 | 0.1548 | - |
176
+ | 0.0290 | 750 | 0.1464 | - |
177
+ | 0.0310 | 800 | 0.1326 | - |
178
+ | 0.0329 | 850 | 0.1304 | - |
179
+ | 0.0348 | 900 | 0.1237 | - |
180
+ | 0.0368 | 950 | 0.1163 | - |
181
+ | 0.0387 | 1000 | 0.1129 | - |
182
+ | 0.0406 | 1050 | 0.1017 | - |
183
+ | 0.0426 | 1100 | 0.0907 | - |
184
+ | 0.0445 | 1150 | 0.0857 | - |
185
+ | 0.0464 | 1200 | 0.0645 | - |
186
+ | 0.0484 | 1250 | 0.0641 | - |
187
+ | 0.0503 | 1300 | 0.0514 | - |
188
+ | 0.0522 | 1350 | 0.0442 | - |
189
+ | 0.0542 | 1400 | 0.0342 | - |
190
+ | 0.0561 | 1450 | 0.0291 | - |
191
+ | 0.0580 | 1500 | 0.0243 | - |
192
+ | 0.0600 | 1550 | 0.0185 | - |
193
+ | 0.0619 | 1600 | 0.0142 | - |
194
+ | 0.0638 | 1650 | 0.0092 | - |
195
+ | 0.0658 | 1700 | 0.0112 | - |
196
+ | 0.0677 | 1750 | 0.0076 | - |
197
+ | 0.0696 | 1800 | 0.0046 | - |
198
+ | 0.0716 | 1850 | 0.0038 | - |
199
+ | 0.0735 | 1900 | 0.0025 | - |
200
+ | 0.0754 | 1950 | 0.0028 | - |
201
+ | 0.0774 | 2000 | 0.0034 | - |
202
+ | 0.0793 | 2050 | 0.0022 | - |
203
+ | 0.0812 | 2100 | 0.0028 | - |
204
+ | 0.0832 | 2150 | 0.0025 | - |
205
+ | 0.0851 | 2200 | 0.0025 | - |
206
+ | 0.0870 | 2250 | 0.0011 | - |
207
+ | 0.0890 | 2300 | 0.0013 | - |
208
+ | 0.0909 | 2350 | 0.0019 | - |
209
+ | 0.0929 | 2400 | 0.0006 | - |
210
+ | 0.0948 | 2450 | 0.0013 | - |
211
+ | 0.0967 | 2500 | 0.0005 | - |
212
+ | 0.0987 | 2550 | 0.0006 | - |
213
+ | 0.1006 | 2600 | 0.0012 | - |
214
+ | 0.1025 | 2650 | 0.0016 | - |
215
+ | 0.1045 | 2700 | 0.0005 | - |
216
+ | 0.1064 | 2750 | 0.0004 | - |
217
+ | 0.1083 | 2800 | 0.0003 | - |
218
+ | 0.1103 | 2850 | 0.0008 | - |
219
+ | 0.1122 | 2900 | 0.001 | - |
220
+ | 0.1141 | 2950 | 0.0018 | - |
221
+ | 0.1161 | 3000 | 0.0005 | - |
222
+ | 0.1180 | 3050 | 0.0002 | - |
223
+ | 0.1199 | 3100 | 0.0005 | - |
224
+ | 0.1219 | 3150 | 0.0006 | - |
225
+ | 0.1238 | 3200 | 0.0017 | - |
226
+ | 0.1257 | 3250 | 0.0009 | - |
227
+ | 0.1277 | 3300 | 0.0026 | - |
228
+ | 0.1296 | 3350 | 0.0008 | - |
229
+ | 0.1315 | 3400 | 0.0009 | - |
230
+ | 0.1335 | 3450 | 0.0013 | - |
231
+ | 0.1354 | 3500 | 0.0009 | - |
232
+ | 0.1373 | 3550 | 0.0011 | - |
233
+ | 0.1393 | 3600 | 0.0008 | - |
234
+ | 0.1412 | 3650 | 0.0004 | - |
235
+ | 0.1431 | 3700 | 0.0009 | - |
236
+ | 0.1451 | 3750 | 0.0008 | - |
237
+ | 0.1470 | 3800 | 0.0012 | - |
238
+ | 0.1489 | 3850 | 0.001 | - |
239
+ | 0.1509 | 3900 | 0.0003 | - |
240
+ | 0.1528 | 3950 | 0.0005 | - |
241
+ | 0.1548 | 4000 | 0.0006 | - |
242
+ | 0.1567 | 4050 | 0.0007 | - |
243
+ | 0.1586 | 4100 | 0.0009 | - |
244
+ | 0.1606 | 4150 | 0.0003 | - |
245
+ | 0.1625 | 4200 | 0.0001 | - |
246
+ | 0.1644 | 4250 | 0.0011 | - |
247
+ | 0.1664 | 4300 | 0.0004 | - |
248
+ | 0.1683 | 4350 | 0.0005 | - |
249
+ | 0.1702 | 4400 | 0.001 | - |
250
+ | 0.1722 | 4450 | 0.0001 | - |
251
+ | 0.1741 | 4500 | 0.0001 | - |
252
+ | 0.1760 | 4550 | 0.0001 | - |
253
+ | 0.1780 | 4600 | 0.0007 | - |
254
+ | 0.1799 | 4650 | 0.0001 | - |
255
+ | 0.1818 | 4700 | 0.0 | - |
256
+ | 0.1838 | 4750 | 0.0 | - |
257
+ | 0.1857 | 4800 | 0.0001 | - |
258
+ | 0.1876 | 4850 | 0.0001 | - |
259
+ | 0.1896 | 4900 | 0.0 | - |
260
+ | 0.1915 | 4950 | 0.0002 | - |
261
+ | 0.1934 | 5000 | 0.0008 | - |
262
+ | 0.1954 | 5050 | 0.0006 | - |
263
+ | 0.1973 | 5100 | 0.0001 | - |
264
+ | 0.1992 | 5150 | 0.0 | - |
265
+ | 0.2012 | 5200 | 0.0 | - |
266
+ | 0.2031 | 5250 | 0.0006 | - |
267
+ | 0.2050 | 5300 | 0.0009 | - |
268
+ | 0.2070 | 5350 | 0.0001 | - |
269
+ | 0.2089 | 5400 | 0.0004 | - |
270
+ | 0.2108 | 5450 | 0.0032 | - |
271
+ | 0.2128 | 5500 | 0.0029 | - |
272
+ | 0.2147 | 5550 | 0.001 | - |
273
+ | 0.2167 | 5600 | 0.0014 | - |
274
+ | 0.2186 | 5650 | 0.0004 | - |
275
+ | 0.2205 | 5700 | 0.0034 | - |
276
+ | 0.2225 | 5750 | 0.0003 | - |
277
+ | 0.2244 | 5800 | 0.0002 | - |
278
+ | 0.2263 | 5850 | 0.0001 | - |
279
+ | 0.2283 | 5900 | 0.0 | - |
280
+ | 0.2302 | 5950 | 0.0 | - |
281
+ | 0.2321 | 6000 | 0.0 | - |
282
+ | 0.2341 | 6050 | 0.0 | - |
283
+ | 0.2360 | 6100 | 0.0 | - |
284
+ | 0.2379 | 6150 | 0.0 | - |
285
+ | 0.2399 | 6200 | 0.0 | - |
286
+ | 0.2418 | 6250 | 0.0 | - |
287
+ | 0.2437 | 6300 | 0.0001 | - |
288
+ | 0.2457 | 6350 | 0.0024 | - |
289
+ | 0.2476 | 6400 | 0.0009 | - |
290
+ | 0.2495 | 6450 | 0.0005 | - |
291
+ | 0.2515 | 6500 | 0.0016 | - |
292
+ | 0.2534 | 6550 | 0.0003 | - |
293
+ | 0.2553 | 6600 | 0.0001 | - |
294
+ | 0.2573 | 6650 | 0.0 | - |
295
+ | 0.2592 | 6700 | 0.0 | - |
296
+ | 0.2611 | 6750 | 0.0 | - |
297
+ | 0.2631 | 6800 | 0.0 | - |
298
+ | 0.2650 | 6850 | 0.0 | - |
299
+ | 0.2669 | 6900 | 0.0 | - |
300
+ | 0.2689 | 6950 | 0.0 | - |
301
+ | 0.2708 | 7000 | 0.0 | - |
302
+ | 0.2727 | 7050 | 0.0 | - |
303
+ | 0.2747 | 7100 | 0.0 | - |
304
+ | 0.2766 | 7150 | 0.0 | - |
305
+ | 0.2786 | 7200 | 0.0 | - |
306
+ | 0.2805 | 7250 | 0.0002 | - |
307
+ | 0.2824 | 7300 | 0.0006 | - |
308
+ | 0.2844 | 7350 | 0.0008 | - |
309
+ | 0.2863 | 7400 | 0.0013 | - |
310
+ | 0.2882 | 7450 | 0.0001 | - |
311
+ | 0.2902 | 7500 | 0.0005 | - |
312
+ | 0.2921 | 7550 | 0.0 | - |
313
+ | 0.2940 | 7600 | 0.0 | - |
314
+ | 0.2960 | 7650 | 0.0 | - |
315
+ | 0.2979 | 7700 | 0.0006 | - |
316
+ | 0.2998 | 7750 | 0.0 | - |
317
+ | 0.3018 | 7800 | 0.0 | - |
318
+ | 0.3037 | 7850 | 0.0 | - |
319
+ | 0.3056 | 7900 | 0.0 | - |
320
+ | 0.3076 | 7950 | 0.0 | - |
321
+ | 0.3095 | 8000 | 0.0 | - |
322
+ | 0.3114 | 8050 | 0.0 | - |
323
+ | 0.3134 | 8100 | 0.0 | - |
324
+ | 0.3153 | 8150 | 0.0 | - |
325
+ | 0.3172 | 8200 | 0.0 | - |
326
+ | 0.3192 | 8250 | 0.0 | - |
327
+ | 0.3211 | 8300 | 0.0 | - |
328
+ | 0.3230 | 8350 | 0.0 | - |
329
+ | 0.3250 | 8400 | 0.0 | - |
330
+ | 0.3269 | 8450 | 0.0 | - |
331
+ | 0.3288 | 8500 | 0.0 | - |
332
+ | 0.3308 | 8550 | 0.0 | - |
333
+ | 0.3327 | 8600 | 0.0 | - |
334
+ | 0.3346 | 8650 | 0.0004 | - |
335
+ | 0.3366 | 8700 | 0.0 | - |
336
+ | 0.3385 | 8750 | 0.0 | - |
337
+ | 0.3405 | 8800 | 0.0 | - |
338
+ | 0.3424 | 8850 | 0.0 | - |
339
+ | 0.3443 | 8900 | 0.0 | - |
340
+ | 0.3463 | 8950 | 0.0 | - |
341
+ | 0.3482 | 9000 | 0.0 | - |
342
+ | 0.3501 | 9050 | 0.0 | - |
343
+ | 0.3521 | 9100 | 0.0001 | - |
344
+ | 0.3540 | 9150 | 0.0037 | - |
345
+ | 0.3559 | 9200 | 0.0013 | - |
346
+ | 0.3579 | 9250 | 0.0007 | - |
347
+ | 0.3598 | 9300 | 0.0032 | - |
348
+ | 0.3617 | 9350 | 0.0006 | - |
349
+ | 0.3637 | 9400 | 0.0007 | - |
350
+ | 0.3656 | 9450 | 0.0 | - |
351
+ | 0.3675 | 9500 | 0.0006 | - |
352
+ | 0.3695 | 9550 | 0.0001 | - |
353
+ | 0.3714 | 9600 | 0.0004 | - |
354
+ | 0.3733 | 9650 | 0.0001 | - |
355
+ | 0.3753 | 9700 | 0.0001 | - |
356
+ | 0.3772 | 9750 | 0.0 | - |
357
+ | 0.3791 | 9800 | 0.0 | - |
358
+ | 0.3811 | 9850 | 0.0 | - |
359
+ | 0.3830 | 9900 | 0.0 | - |
360
+ | 0.3849 | 9950 | 0.0 | - |
361
+ | 0.3869 | 10000 | 0.0 | - |
362
+ | 0.3888 | 10050 | 0.0 | - |
363
+ | 0.3907 | 10100 | 0.0 | - |
364
+ | 0.3927 | 10150 | 0.0 | - |
365
+ | 0.3946 | 10200 | 0.0 | - |
366
+ | 0.3965 | 10250 | 0.0 | - |
367
+ | 0.3985 | 10300 | 0.0 | - |
368
+ | 0.4004 | 10350 | 0.0 | - |
369
+ | 0.4024 | 10400 | 0.0 | - |
370
+ | 0.4043 | 10450 | 0.0 | - |
371
+ | 0.4062 | 10500 | 0.0 | - |
372
+ | 0.4082 | 10550 | 0.0 | - |
373
+ | 0.4101 | 10600 | 0.0 | - |
374
+ | 0.4120 | 10650 | 0.0 | - |
375
+ | 0.4140 | 10700 | 0.0 | - |
376
+ | 0.4159 | 10750 | 0.0 | - |
377
+ | 0.4178 | 10800 | 0.0 | - |
378
+ | 0.4198 | 10850 | 0.0 | - |
379
+ | 0.4217 | 10900 | 0.0001 | - |
380
+ | 0.4236 | 10950 | 0.0 | - |
381
+ | 0.4256 | 11000 | 0.0 | - |
382
+ | 0.4275 | 11050 | 0.0007 | - |
383
+ | 0.4294 | 11100 | 0.0043 | - |
384
+ | 0.4314 | 11150 | 0.0011 | - |
385
+ | 0.4333 | 11200 | 0.0013 | - |
386
+ | 0.4352 | 11250 | 0.0005 | - |
387
+ | 0.4372 | 11300 | 0.0004 | - |
388
+ | 0.4391 | 11350 | 0.0001 | - |
389
+ | 0.4410 | 11400 | 0.0001 | - |
390
+ | 0.4430 | 11450 | 0.0 | - |
391
+ | 0.4449 | 11500 | 0.0001 | - |
392
+ | 0.4468 | 11550 | 0.0 | - |
393
+ | 0.4488 | 11600 | 0.0001 | - |
394
+ | 0.4507 | 11650 | 0.0004 | - |
395
+ | 0.4526 | 11700 | 0.0001 | - |
396
+ | 0.4546 | 11750 | 0.0 | - |
397
+ | 0.4565 | 11800 | 0.0013 | - |
398
+ | 0.4584 | 11850 | 0.0006 | - |
399
+ | 0.4604 | 11900 | 0.0001 | - |
400
+ | 0.4623 | 11950 | 0.0 | - |
401
+ | 0.4643 | 12000 | 0.0 | - |
402
+ | 0.4662 | 12050 | 0.0 | - |
403
+ | 0.4681 | 12100 | 0.0 | - |
404
+ | 0.4701 | 12150 | 0.0 | - |
405
+ | 0.4720 | 12200 | 0.0002 | - |
406
+ | 0.4739 | 12250 | 0.0 | - |
407
+ | 0.4759 | 12300 | 0.0 | - |
408
+ | 0.4778 | 12350 | 0.0 | - |
409
+ | 0.4797 | 12400 | 0.0 | - |
410
+ | 0.4817 | 12450 | 0.0 | - |
411
+ | 0.4836 | 12500 | 0.0 | - |
412
+ | 0.4855 | 12550 | 0.0 | - |
413
+ | 0.4875 | 12600 | 0.0 | - |
414
+ | 0.4894 | 12650 | 0.0 | - |
415
+ | 0.4913 | 12700 | 0.0 | - |
416
+ | 0.4933 | 12750 | 0.0 | - |
417
+ | 0.4952 | 12800 | 0.0 | - |
418
+ | 0.4971 | 12850 | 0.0 | - |
419
+ | 0.4991 | 12900 | 0.0 | - |
420
+ | 0.5010 | 12950 | 0.0 | - |
421
+ | 0.5029 | 13000 | 0.0 | - |
422
+ | 0.5049 | 13050 | 0.0 | - |
423
+ | 0.5068 | 13100 | 0.0 | - |
424
+ | 0.5087 | 13150 | 0.0 | - |
425
+ | 0.5107 | 13200 | 0.0 | - |
426
+ | 0.5126 | 13250 | 0.0 | - |
427
+ | 0.5145 | 13300 | 0.0 | - |
428
+ | 0.5165 | 13350 | 0.0 | - |
429
+ | 0.5184 | 13400 | 0.0 | - |
430
+ | 0.5203 | 13450 | 0.0 | - |
431
+ | 0.5223 | 13500 | 0.0 | - |
432
+ | 0.5242 | 13550 | 0.0 | - |
433
+ | 0.5262 | 13600 | 0.0 | - |
434
+ | 0.5281 | 13650 | 0.0 | - |
435
+ | 0.5300 | 13700 | 0.0 | - |
436
+ | 0.5320 | 13750 | 0.0 | - |
437
+ | 0.5339 | 13800 | 0.0 | - |
438
+ | 0.5358 | 13850 | 0.0 | - |
439
+ | 0.5378 | 13900 | 0.0 | - |
440
+ | 0.5397 | 13950 | 0.0 | - |
441
+ | 0.5416 | 14000 | 0.0 | - |
442
+ | 0.5436 | 14050 | 0.0 | - |
443
+ | 0.5455 | 14100 | 0.0 | - |
444
+ | 0.5474 | 14150 | 0.0 | - |
445
+ | 0.5494 | 14200 | 0.0 | - |
446
+ | 0.5513 | 14250 | 0.0 | - |
447
+ | 0.5532 | 14300 | 0.0 | - |
448
+ | 0.5552 | 14350 | 0.0 | - |
449
+ | 0.5571 | 14400 | 0.0 | - |
450
+ | 0.5590 | 14450 | 0.0 | - |
451
+ | 0.5610 | 14500 | 0.0 | - |
452
+ | 0.5629 | 14550 | 0.0 | - |
453
+ | 0.5648 | 14600 | 0.0 | - |
454
+ | 0.5668 | 14650 | 0.0 | - |
455
+ | 0.5687 | 14700 | 0.0 | - |
456
+ | 0.5706 | 14750 | 0.0 | - |
457
+ | 0.5726 | 14800 | 0.0 | - |
458
+ | 0.5745 | 14850 | 0.0 | - |
459
+ | 0.5764 | 14900 | 0.0 | - |
460
+ | 0.5784 | 14950 | 0.0 | - |
461
+ | 0.5803 | 15000 | 0.0 | - |
462
+ | 0.5823 | 15050 | 0.0 | - |
463
+ | 0.5842 | 15100 | 0.0 | - |
464
+ | 0.5861 | 15150 | 0.0009 | - |
465
+ | 0.5881 | 15200 | 0.0006 | - |
466
+ | 0.5900 | 15250 | 0.0 | - |
467
+ | 0.5919 | 15300 | 0.0 | - |
468
+ | 0.5939 | 15350 | 0.0 | - |
469
+ | 0.5958 | 15400 | 0.0 | - |
470
+ | 0.5977 | 15450 | 0.0 | - |
471
+ | 0.5997 | 15500 | 0.0 | - |
472
+ | 0.6016 | 15550 | 0.0 | - |
473
+ | 0.6035 | 15600 | 0.0 | - |
474
+ | 0.6055 | 15650 | 0.0 | - |
475
+ | 0.6074 | 15700 | 0.0 | - |
476
+ | 0.6093 | 15750 | 0.0006 | - |
477
+ | 0.6113 | 15800 | 0.0007 | - |
478
+ | 0.6132 | 15850 | 0.0 | - |
479
+ | 0.6151 | 15900 | 0.0 | - |
480
+ | 0.6171 | 15950 | 0.0 | - |
481
+ | 0.6190 | 16000 | 0.0 | - |
482
+ | 0.6209 | 16050 | 0.0 | - |
483
+ | 0.6229 | 16100 | 0.0 | - |
484
+ | 0.6248 | 16150 | 0.0 | - |
485
+ | 0.6267 | 16200 | 0.0 | - |
486
+ | 0.6287 | 16250 | 0.0 | - |
487
+ | 0.6306 | 16300 | 0.0 | - |
488
+ | 0.6325 | 16350 | 0.0 | - |
489
+ | 0.6345 | 16400 | 0.0 | - |
490
+ | 0.6364 | 16450 | 0.0 | - |
491
+ | 0.6383 | 16500 | 0.0 | - |
492
+ | 0.6403 | 16550 | 0.0 | - |
493
+ | 0.6422 | 16600 | 0.0 | - |
494
+ | 0.6442 | 16650 | 0.0 | - |
495
+ | 0.6461 | 16700 | 0.0 | - |
496
+ | 0.6480 | 16750 | 0.0 | - |
497
+ | 0.6500 | 16800 | 0.0 | - |
498
+ | 0.6519 | 16850 | 0.0 | - |
499
+ | 0.6538 | 16900 | 0.0 | - |
500
+ | 0.6558 | 16950 | 0.0 | - |
501
+ | 0.6577 | 17000 | 0.0 | - |
502
+ | 0.6596 | 17050 | 0.0 | - |
503
+ | 0.6616 | 17100 | 0.0 | - |
504
+ | 0.6635 | 17150 | 0.0 | - |
505
+ | 0.6654 | 17200 | 0.0 | - |
506
+ | 0.6674 | 17250 | 0.0 | - |
507
+ | 0.6693 | 17300 | 0.0 | - |
508
+ | 0.6712 | 17350 | 0.0 | - |
509
+ | 0.6732 | 17400 | 0.0 | - |
510
+ | 0.6751 | 17450 | 0.0 | - |
511
+ | 0.6770 | 17500 | 0.0 | - |
512
+ | 0.6790 | 17550 | 0.0 | - |
513
+ | 0.6809 | 17600 | 0.0 | - |
514
+ | 0.6828 | 17650 | 0.0 | - |
515
+ | 0.6848 | 17700 | 0.0 | - |
516
+ | 0.6867 | 17750 | 0.0 | - |
517
+ | 0.6886 | 17800 | 0.0 | - |
518
+ | 0.6906 | 17850 | 0.0 | - |
519
+ | 0.6925 | 17900 | 0.0 | - |
520
+ | 0.6944 | 17950 | 0.0 | - |
521
+ | 0.6964 | 18000 | 0.0 | - |
522
+ | 0.6983 | 18050 | 0.0007 | - |
523
+ | 0.7002 | 18100 | 0.0 | - |
524
+ | 0.7022 | 18150 | 0.0 | - |
525
+ | 0.7041 | 18200 | 0.0 | - |
526
+ | 0.7061 | 18250 | 0.0 | - |
527
+ | 0.7080 | 18300 | 0.0 | - |
528
+ | 0.7099 | 18350 | 0.0 | - |
529
+ | 0.7119 | 18400 | 0.0 | - |
530
+ | 0.7138 | 18450 | 0.0 | - |
531
+ | 0.7157 | 18500 | 0.0001 | - |
532
+ | 0.7177 | 18550 | 0.0 | - |
533
+ | 0.7196 | 18600 | 0.0 | - |
534
+ | 0.7215 | 18650 | 0.0004 | - |
535
+ | 0.7235 | 18700 | 0.0 | - |
536
+ | 0.7254 | 18750 | 0.0 | - |
537
+ | 0.7273 | 18800 | 0.0 | - |
538
+ | 0.7293 | 18850 | 0.0 | - |
539
+ | 0.7312 | 18900 | 0.0 | - |
540
+ | 0.7331 | 18950 | 0.0 | - |
541
+ | 0.7351 | 19000 | 0.0 | - |
542
+ | 0.7370 | 19050 | 0.0 | - |
543
+ | 0.7389 | 19100 | 0.0 | - |
544
+ | 0.7409 | 19150 | 0.0 | - |
545
+ | 0.7428 | 19200 | 0.0 | - |
546
+ | 0.7447 | 19250 | 0.0 | - |
547
+ | 0.7467 | 19300 | 0.0 | - |
548
+ | 0.7486 | 19350 | 0.0 | - |
549
+ | 0.7505 | 19400 | 0.0 | - |
550
+ | 0.7525 | 19450 | 0.0 | - |
551
+ | 0.7544 | 19500 | 0.0 | - |
552
+ | 0.7563 | 19550 | 0.0 | - |
553
+ | 0.7583 | 19600 | 0.0 | - |
554
+ | 0.7602 | 19650 | 0.0 | - |
555
+ | 0.7621 | 19700 | 0.0 | - |
556
+ | 0.7641 | 19750 | 0.0 | - |
557
+ | 0.7660 | 19800 | 0.0 | - |
558
+ | 0.7680 | 19850 | 0.0 | - |
559
+ | 0.7699 | 19900 | 0.0 | - |
560
+ | 0.7718 | 19950 | 0.0 | - |
561
+ | 0.7738 | 20000 | 0.0 | - |
562
+ | 0.7757 | 20050 | 0.0 | - |
563
+ | 0.7776 | 20100 | 0.0 | - |
564
+ | 0.7796 | 20150 | 0.0 | - |
565
+ | 0.7815 | 20200 | 0.0 | - |
566
+ | 0.7834 | 20250 | 0.0 | - |
567
+ | 0.7854 | 20300 | 0.0 | - |
568
+ | 0.7873 | 20350 | 0.0 | - |
569
+ | 0.7892 | 20400 | 0.0 | - |
570
+ | 0.7912 | 20450 | 0.0 | - |
571
+ | 0.7931 | 20500 | 0.0 | - |
572
+ | 0.7950 | 20550 | 0.0 | - |
573
+ | 0.7970 | 20600 | 0.0 | - |
574
+ | 0.7989 | 20650 | 0.0 | - |
575
+ | 0.8008 | 20700 | 0.0 | - |
576
+ | 0.8028 | 20750 | 0.0 | - |
577
+ | 0.8047 | 20800 | 0.0 | - |
578
+ | 0.8066 | 20850 | 0.0 | - |
579
+ | 0.8086 | 20900 | 0.0 | - |
580
+ | 0.8105 | 20950 | 0.0 | - |
581
+ | 0.8124 | 21000 | 0.0 | - |
582
+ | 0.8144 | 21050 | 0.0 | - |
583
+ | 0.8163 | 21100 | 0.0 | - |
584
+ | 0.8182 | 21150 | 0.0 | - |
585
+ | 0.8202 | 21200 | 0.0 | - |
586
+ | 0.8221 | 21250 | 0.0 | - |
587
+ | 0.8240 | 21300 | 0.0 | - |
588
+ | 0.8260 | 21350 | 0.0 | - |
589
+ | 0.8279 | 21400 | 0.0 | - |
590
+ | 0.8299 | 21450 | 0.0 | - |
591
+ | 0.8318 | 21500 | 0.0 | - |
592
+ | 0.8337 | 21550 | 0.0 | - |
593
+ | 0.8357 | 21600 | 0.0 | - |
594
+ | 0.8376 | 21650 | 0.0 | - |
595
+ | 0.8395 | 21700 | 0.0 | - |
596
+ | 0.8415 | 21750 | 0.0 | - |
597
+ | 0.8434 | 21800 | 0.0 | - |
598
+ | 0.8453 | 21850 | 0.0 | - |
599
+ | 0.8473 | 21900 | 0.0 | - |
600
+ | 0.8492 | 21950 | 0.0 | - |
601
+ | 0.8511 | 22000 | 0.0 | - |
602
+ | 0.8531 | 22050 | 0.0 | - |
603
+ | 0.8550 | 22100 | 0.0 | - |
604
+ | 0.8569 | 22150 | 0.0 | - |
605
+ | 0.8589 | 22200 | 0.0 | - |
606
+ | 0.8608 | 22250 | 0.0 | - |
607
+ | 0.8627 | 22300 | 0.0 | - |
608
+ | 0.8647 | 22350 | 0.0 | - |
609
+ | 0.8666 | 22400 | 0.0 | - |
610
+ | 0.8685 | 22450 | 0.0 | - |
611
+ | 0.8705 | 22500 | 0.0 | - |
612
+ | 0.8724 | 22550 | 0.0 | - |
613
+ | 0.8743 | 22600 | 0.0 | - |
614
+ | 0.8763 | 22650 | 0.0 | - |
615
+ | 0.8782 | 22700 | 0.0 | - |
616
+ | 0.8801 | 22750 | 0.0 | - |
617
+ | 0.8821 | 22800 | 0.0 | - |
618
+ | 0.8840 | 22850 | 0.0 | - |
619
+ | 0.8859 | 22900 | 0.0 | - |
620
+ | 0.8879 | 22950 | 0.0 | - |
621
+ | 0.8898 | 23000 | 0.0 | - |
622
+ | 0.8918 | 23050 | 0.0 | - |
623
+ | 0.8937 | 23100 | 0.0 | - |
624
+ | 0.8956 | 23150 | 0.0 | - |
625
+ | 0.8976 | 23200 | 0.0 | - |
626
+ | 0.8995 | 23250 | 0.0 | - |
627
+ | 0.9014 | 23300 | 0.0 | - |
628
+ | 0.9034 | 23350 | 0.0 | - |
629
+ | 0.9053 | 23400 | 0.0 | - |
630
+ | 0.9072 | 23450 | 0.0 | - |
631
+ | 0.9092 | 23500 | 0.0 | - |
632
+ | 0.9111 | 23550 | 0.0 | - |
633
+ | 0.9130 | 23600 | 0.0 | - |
634
+ | 0.9150 | 23650 | 0.0 | - |
635
+ | 0.9169 | 23700 | 0.0 | - |
636
+ | 0.9188 | 23750 | 0.0 | - |
637
+ | 0.9208 | 23800 | 0.0 | - |
638
+ | 0.9227 | 23850 | 0.0 | - |
639
+ | 0.9246 | 23900 | 0.0 | - |
640
+ | 0.9266 | 23950 | 0.0 | - |
641
+ | 0.9285 | 24000 | 0.0 | - |
642
+ | 0.9304 | 24050 | 0.0 | - |
643
+ | 0.9324 | 24100 | 0.0 | - |
644
+ | 0.9343 | 24150 | 0.0 | - |
645
+ | 0.9362 | 24200 | 0.0 | - |
646
+ | 0.9382 | 24250 | 0.0 | - |
647
+ | 0.9401 | 24300 | 0.0 | - |
648
+ | 0.9420 | 24350 | 0.0 | - |
649
+ | 0.9440 | 24400 | 0.0 | - |
650
+ | 0.9459 | 24450 | 0.0 | - |
651
+ | 0.9478 | 24500 | 0.0 | - |
652
+ | 0.9498 | 24550 | 0.0 | - |
653
+ | 0.9517 | 24600 | 0.0 | - |
654
+ | 0.9537 | 24650 | 0.0 | - |
655
+ | 0.9556 | 24700 | 0.0 | - |
656
+ | 0.9575 | 24750 | 0.0 | - |
657
+ | 0.9595 | 24800 | 0.0 | - |
658
+ | 0.9614 | 24850 | 0.0 | - |
659
+ | 0.9633 | 24900 | 0.0 | - |
660
+ | 0.9653 | 24950 | 0.0 | - |
661
+ | 0.9672 | 25000 | 0.0 | - |
662
+ | 0.9691 | 25050 | 0.0 | - |
663
+ | 0.9711 | 25100 | 0.0 | - |
664
+ | 0.9730 | 25150 | 0.0 | - |
665
+ | 0.9749 | 25200 | 0.0 | - |
666
+ | 0.9769 | 25250 | 0.0 | - |
667
+ | 0.9788 | 25300 | 0.0 | - |
668
+ | 0.9807 | 25350 | 0.0 | - |
669
+ | 0.9827 | 25400 | 0.0 | - |
670
+ | 0.9846 | 25450 | 0.0 | - |
671
+ | 0.9865 | 25500 | 0.0 | - |
672
+ | 0.9885 | 25550 | 0.0 | - |
673
+ | 0.9904 | 25600 | 0.0 | - |
674
+ | 0.9923 | 25650 | 0.0 | - |
675
+ | 0.9943 | 25700 | 0.0 | - |
676
+ | 0.9962 | 25750 | 0.0 | - |
677
+ | 0.9981 | 25800 | 0.0 | - |
678
+
679
+ ### Framework Versions
680
+ - Python: 3.10.12
681
+ - SetFit: 1.1.0
682
+ - Sentence Transformers: 3.3.1
683
+ - Transformers: 4.42.2
684
+ - PyTorch: 2.5.1+cu121
685
+ - Datasets: 3.2.0
686
+ - Tokenizers: 0.19.1
687
+
688
+ ## Citation
689
+
690
+ ### BibTeX
691
+ ```bibtex
692
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
693
+ doi = {10.48550/ARXIV.2209.11055},
694
+ url = {https://arxiv.org/abs/2209.11055},
695
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
696
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
697
+ title = {Efficient Few-Shot Learning Without Prompts},
698
+ publisher = {arXiv},
699
+ year = {2022},
700
+ copyright = {Creative Commons Attribution 4.0 International}
701
+ }
702
+ ```
703
+
704
+ <!--
705
+ ## Glossary
706
+
707
+ *Clearly define terms in order to be accessible across audiences.*
708
+ -->
709
+
710
+ <!--
711
+ ## Model Card Authors
712
+
713
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
714
+ -->
715
+
716
+ <!--
717
+ ## Model Card Contact
718
+
719
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
720
+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 384,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 1536,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 6,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.42.2",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 30522
26
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.42.2",
5
+ "pytorch": "2.5.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "labels": [
3
+ "neutral",
4
+ "opposed",
5
+ "supportive"
6
+ ],
7
+ "normalize_embeddings": false
8
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:238c09c03d7e61aeeb460d05297ae66617b45df4baf5e2c77898c499f21181f7
3
+ size 90864192
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:56d477543e1b21c92feccd455c777fceb20ebb5efc6d7234c6b17c87b093c5b2
3
+ size 10207
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 256,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
49
+ "max_length": 128,
50
+ "model_max_length": 256,
51
+ "never_split": null,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "[PAD]",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff