Hugging Face's logo

janmariakowalski
/
LiderzyAI-homestyle-reklamacje

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
janmariakowalski commited on
Commit
9697027
·
verified ·
1 Parent(s): 5e07c72

Push model using huggingface_hub.

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +424 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +9 -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 +57 -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": true,
4
+ "pooling_mode_mean_tokens": false,
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 +1,424 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: BAAI/bge-small-en-v1.5
3
+ library_name: setfit
4
+ metrics:
5
+ - accuracy
6
+ pipeline_tag: text-classification
7
+ tags:
8
+ - setfit
9
+ - sentence-transformers
10
+ - text-classification
11
+ - generated_from_setfit_trainer
12
+ widget:
13
+ - text: W elementach regału brakuje kilku otworów montażowych, uniemożliwiających
14
+ prawidłowe złożenie.
15
+ - text: Półki regału Biblioteka są zbyt wąskie.
16
+ - text: Łóżko drewniane posiada wadę konstrukcji.
17
+ - text: Noga krzesła drewnianego Country jest złamana.
18
+ - text: Konstrukcja łóżka piętrowego jest wadliwa, elementy nie pasują do siebie.
19
+ inference: true
20
+ model-index:
21
+ - name: SetFit with BAAI/bge-small-en-v1.5
22
+ results:
23
+ - task:
24
+ type: text-classification
25
+ name: Text Classification
26
+ dataset:
27
+ name: Unknown
28
+ type: unknown
29
+ split: test
30
+ metrics:
31
+ - type: accuracy
32
+ value: 0.7606837606837606
33
+ name: Accuracy
34
+ ---
35
+
36
+ # SetFit with BAAI/bge-small-en-v1.5
37
+
38
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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.
39
+
40
+ The model has been trained using an efficient few-shot learning technique that involves:
41
+
42
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
43
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
44
+
45
+ ## Model Details
46
+
47
+ ### Model Description
48
+ - **Model Type:** SetFit
49
+ - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
50
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
51
+ - **Maximum Sequence Length:** 512 tokens
52
+ - **Number of Classes:** 4 classes
53
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
54
+ <!-- - **Language:** Unknown -->
55
+ <!-- - **License:** Unknown -->
56
+
57
+ ### Model Sources
58
+
59
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
60
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
61
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
62
+
63
+ ### Model Labels
64
+ | Label | Examples |
65
+ |:---------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
66
+ | uszkodzenia | <ul><li>'Drzwiczki szafki na buty Shoe są uszkodzone.'</li><li>'Elementy zestawu mebli ogrodowych Relax są zardzewiałe.'</li><li>'Śruby w stoliku kawowym Loft są obluzowane.'</li></ul> |
67
+ | błędny montaż | <ul><li>'Szuflady komody wąskiej są zamontowane krzywo i się zacinają.'</li><li>'Drążek na ubrania w szafie jest źle zamontowany i może się wypaść.'</li><li>'Prowadnice szafy przesuwnej są źle zamontowane, przez co drzwi nie przesuwają się płynnie.'</li></ul> |
68
+ | wady fabryczne | <ul><li>'Brakuje elementów w komodzie Wygodny.'</li><li>'Brakuje drzwiczek w szafie Współczesny.'</li><li>'Tapicerka krzesła jest rozdarcia.'</li></ul> |
69
+ | niezgodność towaru z zamówieniem | <ul><li>'Kolor stołu dębowego Olbrzym jest inny niż ten, który został zamówiony.'</li><li>'Kolor fotela skórzanego Leather jest inny niż ten, który został zamówiony.'</li><li>'Wysokość biurka białego White jest niższa niż ta, która była w zamówieniu.'</li></ul> |
70
+
71
+ ## Evaluation
72
+
73
+ ### Metrics
74
+ | Label | Accuracy |
75
+ |:--------|:---------|
76
+ | **all** | 0.7607 |
77
+
78
+ ## Uses
79
+
80
+ ### Direct Use for Inference
81
+
82
+ First install the SetFit library:
83
+
84
+ ```bash
85
+ pip install setfit
86
+ ```
87
+
88
+ Then you can load this model and run inference.
89
+
90
+ ```python
91
+ from setfit import SetFitModel
92
+
93
+ # Download from the 🤗 Hub
94
+ model = SetFitModel.from_pretrained("setfit_model_id")
95
+ # Run inference
96
+ preds = model("Półki regału Biblioteka są zbyt wąskie.")
97
+ ```
98
+
99
+ <!--
100
+ ### Downstream Use
101
+
102
+ *List how someone could finetune this model on their own dataset.*
103
+ -->
104
+
105
+ <!--
106
+ ### Out-of-Scope Use
107
+
108
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
109
+ -->
110
+
111
+ <!--
112
+ ## Bias, Risks and Limitations
113
+
114
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
115
+ -->
116
+
117
+ <!--
118
+ ### Recommendations
119
+
120
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
121
+ -->
122
+
123
+ ## Training Details
124
+
125
+ ### Training Set Metrics
126
+ | Training set | Min | Median | Max |
127
+ |:-------------|:----|:--------|:----|
128
+ | Word count | 4 | 10.1875 | 39 |
129
+
130
+ | Label | Training Sample Count |
131
+ |:---------------------------------|:----------------------|
132
+ | uszkodzenia | 24 |
133
+ | wady fabryczne | 24 |
134
+ | niezgodność towaru z zamówieniem | 24 |
135
+ | błędny montaż | 24 |
136
+
137
+ ### Training Hyperparameters
138
+ - batch_size: (32, 32)
139
+ - num_epochs: (10, 10)
140
+ - max_steps: -1
141
+ - sampling_strategy: oversampling
142
+ - body_learning_rate: (2e-05, 1e-05)
143
+ - head_learning_rate: 0.01
144
+ - loss: CosineSimilarityLoss
145
+ - distance_metric: cosine_distance
146
+ - margin: 0.25
147
+ - end_to_end: False
148
+ - use_amp: False
149
+ - warmup_proportion: 0.1
150
+ - l2_weight: 0.01
151
+ - seed: 42
152
+ - eval_max_steps: -1
153
+ - load_best_model_at_end: False
154
+
155
+ ### Training Results
156
+ | Epoch | Step | Training Loss | Validation Loss |
157
+ |:------:|:----:|:-------------:|:---------------:|
158
+ | 0.0104 | 1 | 0.1965 | - |
159
+ | 0.1042 | 10 | 0.2713 | - |
160
+ | 0.2083 | 20 | 0.2394 | - |
161
+ | 0.3125 | 30 | 0.2249 | - |
162
+ | 0.4167 | 40 | 0.2265 | - |
163
+ | 0.5208 | 50 | 0.2153 | - |
164
+ | 0.625 | 60 | 0.2043 | - |
165
+ | 0.7292 | 70 | 0.2033 | - |
166
+ | 0.8333 | 80 | 0.204 | - |
167
+ | 0.9375 | 90 | 0.1648 | - |
168
+ | 1.0417 | 100 | 0.1452 | - |
169
+ | 1.1458 | 110 | 0.1219 | - |
170
+ | 1.25 | 120 | 0.1062 | - |
171
+ | 1.3542 | 130 | 0.0921 | - |
172
+ | 1.4583 | 140 | 0.0803 | - |
173
+ | 1.5625 | 150 | 0.0559 | - |
174
+ | 1.6667 | 160 | 0.0339 | - |
175
+ | 1.7708 | 170 | 0.0258 | - |
176
+ | 1.875 | 180 | 0.0153 | - |
177
+ | 1.9792 | 190 | 0.0095 | - |
178
+ | 2.0833 | 200 | 0.0074 | - |
179
+ | 2.1875 | 210 | 0.0076 | - |
180
+ | 2.2917 | 220 | 0.0058 | - |
181
+ | 2.3958 | 230 | 0.005 | - |
182
+ | 2.5 | 240 | 0.0047 | - |
183
+ | 2.6042 | 250 | 0.0043 | - |
184
+ | 2.7083 | 260 | 0.0041 | - |
185
+ | 2.8125 | 270 | 0.0038 | - |
186
+ | 2.9167 | 280 | 0.0035 | - |
187
+ | 3.0208 | 290 | 0.0034 | - |
188
+ | 3.125 | 300 | 0.0033 | - |
189
+ | 3.2292 | 310 | 0.0035 | - |
190
+ | 3.3333 | 320 | 0.0028 | - |
191
+ | 3.4375 | 330 | 0.0031 | - |
192
+ | 3.5417 | 340 | 0.0028 | - |
193
+ | 3.6458 | 350 | 0.0027 | - |
194
+ | 3.75 | 360 | 0.0025 | - |
195
+ | 3.8542 | 370 | 0.0025 | - |
196
+ | 3.9583 | 380 | 0.0024 | - |
197
+ | 4.0625 | 390 | 0.0023 | - |
198
+ | 4.1667 | 400 | 0.0023 | - |
199
+ | 4.2708 | 410 | 0.0022 | - |
200
+ | 4.375 | 420 | 0.0021 | - |
201
+ | 4.4792 | 430 | 0.0022 | - |
202
+ | 4.5833 | 440 | 0.002 | - |
203
+ | 4.6875 | 450 | 0.0022 | - |
204
+ | 4.7917 | 460 | 0.0019 | - |
205
+ | 4.8958 | 470 | 0.0021 | - |
206
+ | 5.0 | 480 | 0.0018 | - |
207
+ | 5.1042 | 490 | 0.002 | - |
208
+ | 5.2083 | 500 | 0.002 | - |
209
+ | 5.3125 | 510 | 0.0017 | - |
210
+ | 5.4167 | 520 | 0.002 | - |
211
+ | 5.5208 | 530 | 0.0017 | - |
212
+ | 5.625 | 540 | 0.0018 | - |
213
+ | 5.7292 | 550 | 0.0016 | - |
214
+ | 5.8333 | 560 | 0.0016 | - |
215
+ | 5.9375 | 570 | 0.0015 | - |
216
+ | 6.0417 | 580 | 0.0014 | - |
217
+ | 6.1458 | 590 | 0.0017 | - |
218
+ | 6.25 | 600 | 0.0016 | - |
219
+ | 6.3542 | 610 | 0.0017 | - |
220
+ | 6.4583 | 620 | 0.0016 | - |
221
+ | 6.5625 | 630 | 0.0017 | - |
222
+ | 6.6667 | 640 | 0.0014 | - |
223
+ | 6.7708 | 650 | 0.0014 | - |
224
+ | 6.875 | 660 | 0.0016 | - |
225
+ | 6.9792 | 670 | 0.0015 | - |
226
+ | 7.0833 | 680 | 0.0015 | - |
227
+ | 7.1875 | 690 | 0.0014 | - |
228
+ | 7.2917 | 700 | 0.0015 | - |
229
+ | 7.3958 | 710 | 0.0014 | - |
230
+ | 7.5 | 720 | 0.0015 | - |
231
+ | 7.6042 | 730 | 0.0014 | - |
232
+ | 7.7083 | 740 | 0.0014 | - |
233
+ | 7.8125 | 750 | 0.0014 | - |
234
+ | 7.9167 | 760 | 0.0015 | - |
235
+ | 8.0208 | 770 | 0.0013 | - |
236
+ | 8.125 | 780 | 0.0014 | - |
237
+ | 8.2292 | 790 | 0.0014 | - |
238
+ | 8.3333 | 800 | 0.0014 | - |
239
+ | 8.4375 | 810 | 0.0014 | - |
240
+ | 8.5417 | 820 | 0.0014 | - |
241
+ | 8.6458 | 830 | 0.0014 | - |
242
+ | 8.75 | 840 | 0.0013 | - |
243
+ | 8.8542 | 850 | 0.0013 | - |
244
+ | 8.9583 | 860 | 0.0013 | - |
245
+ | 9.0625 | 870 | 0.0013 | - |
246
+ | 9.1667 | 880 | 0.0014 | - |
247
+ | 9.2708 | 890 | 0.0013 | - |
248
+ | 9.375 | 900 | 0.0012 | - |
249
+ | 9.4792 | 910 | 0.0013 | - |
250
+ | 9.5833 | 920 | 0.0012 | - |
251
+ | 9.6875 | 930 | 0.0013 | - |
252
+ | 9.7917 | 940 | 0.0013 | - |
253
+ | 9.8958 | 950 | 0.0013 | - |
254
+ | 10.0 | 960 | 0.0014 | - |
255
+ | 0.0008 | 1 | 0.2276 | - |
256
+ | 0.0083 | 10 | 0.2361 | - |
257
+ | 0.0167 | 20 | 0.1815 | - |
258
+ | 0.0250 | 30 | 0.2036 | - |
259
+ | 0.0333 | 40 | 0.1783 | - |
260
+ | 0.0416 | 50 | 0.1486 | - |
261
+ | 0.0500 | 60 | 0.191 | - |
262
+ | 0.0583 | 70 | 0.1741 | - |
263
+ | 0.0104 | 1 | 0.0021 | - |
264
+ | 0.1042 | 10 | 0.002 | - |
265
+ | 0.2083 | 20 | 0.0015 | - |
266
+ | 0.3125 | 30 | 0.0015 | - |
267
+ | 0.4167 | 40 | 0.0013 | - |
268
+ | 0.5208 | 50 | 0.0013 | - |
269
+ | 0.625 | 60 | 0.0012 | - |
270
+ | 0.7292 | 70 | 0.0011 | - |
271
+ | 0.8333 | 80 | 0.0012 | - |
272
+ | 0.9375 | 90 | 0.0011 | - |
273
+ | 1.0417 | 100 | 0.001 | - |
274
+ | 1.1458 | 110 | 0.001 | - |
275
+ | 1.25 | 120 | 0.0009 | - |
276
+ | 1.3542 | 130 | 0.0009 | - |
277
+ | 1.4583 | 140 | 0.0008 | - |
278
+ | 1.5625 | 150 | 0.0009 | - |
279
+ | 1.6667 | 160 | 0.0009 | - |
280
+ | 1.7708 | 170 | 0.0008 | - |
281
+ | 1.875 | 180 | 0.0008 | - |
282
+ | 1.9792 | 190 | 0.0007 | - |
283
+ | 2.0833 | 200 | 0.0007 | - |
284
+ | 2.1875 | 210 | 0.0007 | - |
285
+ | 2.2917 | 220 | 0.0007 | - |
286
+ | 2.3958 | 230 | 0.0006 | - |
287
+ | 2.5 | 240 | 0.0007 | - |
288
+ | 2.6042 | 250 | 0.0007 | - |
289
+ | 2.7083 | 260 | 0.0007 | - |
290
+ | 2.8125 | 270 | 0.0006 | - |
291
+ | 2.9167 | 280 | 0.0006 | - |
292
+ | 3.0208 | 290 | 0.0006 | - |
293
+ | 3.125 | 300 | 0.0006 | - |
294
+ | 3.2292 | 310 | 0.0006 | - |
295
+ | 3.3333 | 320 | 0.0006 | - |
296
+ | 3.4375 | 330 | 0.0006 | - |
297
+ | 3.5417 | 340 | 0.0006 | - |
298
+ | 3.6458 | 350 | 0.0005 | - |
299
+ | 3.75 | 360 | 0.0005 | - |
300
+ | 3.8542 | 370 | 0.0006 | - |
301
+ | 3.9583 | 380 | 0.0005 | - |
302
+ | 4.0625 | 390 | 0.0005 | - |
303
+ | 4.1667 | 400 | 0.0005 | - |
304
+ | 4.2708 | 410 | 0.0005 | - |
305
+ | 4.375 | 420 | 0.0006 | - |
306
+ | 4.4792 | 430 | 0.0005 | - |
307
+ | 4.5833 | 440 | 0.0005 | - |
308
+ | 4.6875 | 450 | 0.0005 | - |
309
+ | 4.7917 | 460 | 0.0005 | - |
310
+ | 4.8958 | 470 | 0.0005 | - |
311
+ | 5.0 | 480 | 0.0004 | - |
312
+ | 5.1042 | 490 | 0.0005 | - |
313
+ | 5.2083 | 500 | 0.0005 | - |
314
+ | 5.3125 | 510 | 0.0004 | - |
315
+ | 5.4167 | 520 | 0.0005 | - |
316
+ | 5.5208 | 530 | 0.0005 | - |
317
+ | 5.625 | 540 | 0.0005 | - |
318
+ | 5.7292 | 550 | 0.0005 | - |
319
+ | 5.8333 | 560 | 0.0004 | - |
320
+ | 5.9375 | 570 | 0.0004 | - |
321
+ | 6.0417 | 580 | 0.0004 | - |
322
+ | 6.1458 | 590 | 0.0004 | - |
323
+ | 6.25 | 600 | 0.0004 | - |
324
+ | 6.3542 | 610 | 0.0005 | - |
325
+ | 6.4583 | 620 | 0.0004 | - |
326
+ | 6.5625 | 630 | 0.0005 | - |
327
+ | 6.6667 | 640 | 0.0004 | - |
328
+ | 6.7708 | 650 | 0.0004 | - |
329
+ | 6.875 | 660 | 0.0004 | - |
330
+ | 6.9792 | 670 | 0.0004 | - |
331
+ | 7.0833 | 680 | 0.0004 | - |
332
+ | 7.1875 | 690 | 0.0004 | - |
333
+ | 7.2917 | 700 | 0.0004 | - |
334
+ | 7.3958 | 710 | 0.0004 | - |
335
+ | 7.5 | 720 | 0.0004 | - |
336
+ | 7.6042 | 730 | 0.0004 | - |
337
+ | 7.7083 | 740 | 0.0004 | - |
338
+ | 7.8125 | 750 | 0.0004 | - |
339
+ | 7.9167 | 760 | 0.0004 | - |
340
+ | 8.0208 | 770 | 0.0004 | - |
341
+ | 8.125 | 780 | 0.0004 | - |
342
+ | 8.2292 | 790 | 0.0004 | - |
343
+ | 8.3333 | 800 | 0.0004 | - |
344
+ | 8.4375 | 810 | 0.0004 | - |
345
+ | 8.5417 | 820 | 0.0004 | - |
346
+ | 8.6458 | 830 | 0.0004 | - |
347
+ | 8.75 | 840 | 0.0004 | - |
348
+ | 8.8542 | 850 | 0.0004 | - |
349
+ | 8.9583 | 860 | 0.0004 | - |
350
+ | 9.0625 | 870 | 0.0004 | - |
351
+ | 9.1667 | 880 | 0.0004 | - |
352
+ | 9.2708 | 890 | 0.0004 | - |
353
+ | 9.375 | 900 | 0.0004 | - |
354
+ | 9.4792 | 910 | 0.0004 | - |
355
+ | 9.5833 | 920 | 0.0004 | - |
356
+ | 9.6875 | 930 | 0.0004 | - |
357
+ | 9.7917 | 940 | 0.0004 | - |
358
+ | 9.8958 | 950 | 0.0004 | - |
359
+ | 10.0 | 960 | 0.0004 | - |
360
+ | 0.0046 | 1 | 0.049 | - |
361
+ | 0.4630 | 100 | 0.035 | - |
362
+ | 0.9259 | 200 | 0.0097 | - |
363
+ | 1.3889 | 300 | 0.0007 | - |
364
+ | 1.8519 | 400 | 0.0004 | - |
365
+ | 2.3148 | 500 | 0.0004 | - |
366
+ | 2.7778 | 600 | 0.0004 | - |
367
+ | 3.2407 | 700 | 0.0004 | - |
368
+ | 3.7037 | 800 | 0.0004 | - |
369
+ | 4.1667 | 900 | 0.0004 | - |
370
+ | 4.6296 | 1000 | 0.0003 | - |
371
+ | 5.0926 | 1100 | 0.0003 | - |
372
+ | 5.5556 | 1200 | 0.0003 | - |
373
+ | 6.0185 | 1300 | 0.0003 | - |
374
+ | 6.4815 | 1400 | 0.0003 | - |
375
+ | 6.9444 | 1500 | 0.0003 | - |
376
+ | 7.4074 | 1600 | 0.0003 | - |
377
+ | 7.8704 | 1700 | 0.0003 | - |
378
+ | 8.3333 | 1800 | 0.0003 | - |
379
+ | 8.7963 | 1900 | 0.0003 | - |
380
+ | 9.2593 | 2000 | 0.0003 | - |
381
+ | 9.7222 | 2100 | 0.0003 | - |
382
+
383
+ ### Framework Versions
384
+ - Python: 3.11.0
385
+ - SetFit: 1.1.0
386
+ - Sentence Transformers: 3.3.1
387
+ - Transformers: 4.44.2
388
+ - PyTorch: 2.4.1
389
+ - Datasets: 2.19.0
390
+ - Tokenizers: 0.19.1
391
+
392
+ ## Citation
393
+
394
+ ### BibTeX
395
+ ```bibtex
396
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
397
+ doi = {10.48550/ARXIV.2209.11055},
398
+ url = {https://arxiv.org/abs/2209.11055},
399
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
400
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
401
+ title = {Efficient Few-Shot Learning Without Prompts},
402
+ publisher = {arXiv},
403
+ year = {2022},
404
+ copyright = {Creative Commons Attribution 4.0 International}
405
+ }
406
+ ```
407
+
408
+ <!--
409
+ ## Glossary
410
+
411
+ *Clearly define terms in order to be accessible across audiences.*
412
+ -->
413
+
414
+ <!--
415
+ ## Model Card Authors
416
+
417
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
418
+ -->
419
+
420
+ <!--
421
+ ## Model Card Contact
422
+
423
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
424
+ -->
config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "BAAI/bge-small-en-v1.5",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 384,
11
+ "id2label": {
12
+ "0": "LABEL_0"
13
+ },
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 1536,
16
+ "label2id": {
17
+ "LABEL_0": 0
18
+ },
19
+ "layer_norm_eps": 1e-12,
20
+ "max_position_embeddings": 512,
21
+ "model_type": "bert",
22
+ "num_attention_heads": 12,
23
+ "num_hidden_layers": 12,
24
+ "pad_token_id": 0,
25
+ "position_embedding_type": "absolute",
26
+ "torch_dtype": "float32",
27
+ "transformers_version": "4.44.2",
28
+ "type_vocab_size": 2,
29
+ "use_cache": true,
30
+ "vocab_size": 30522
31
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.4.1"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "normalize_embeddings": false,
3
+ "labels": [
4
+ "uszkodzenia",
5
+ "wady fabryczne",
6
+ "niezgodno\u015b\u0107 towaru z zam\u00f3wieniem",
7
+ "b\u0142\u0119dny monta\u017c"
8
+ ]
9
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4311eb5c18640caba9acc56709d4a03bcc4dc0083e8664a6f37adadd5b0be116
3
+ size 133462128
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1d2aa0524b52f8838d0aa130ed7ae8e5cfa7a6d9e038b44f39aabe389e5ba0cf
3
+ size 13191
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": 512,
3
+ "do_lower_case": true
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 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ "model_max_length": 512,
50
+ "never_split": null,
51
+ "pad_token": "[PAD]",
52
+ "sep_token": "[SEP]",
53
+ "strip_accents": null,
54
+ "tokenize_chinese_chars": true,
55
+ "tokenizer_class": "BertTokenizer",
56
+ "unk_token": "[UNK]"
57
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff