File size: 23,662 Bytes
d9666ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- accuracy
widget:
- text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya
    brawlstar ga
- text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan
    game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon
    perbaiki jaringan
- text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja
    pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang
    masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki
    ya min klok grafik gameplay udah bagus
- text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan
    jaringan bagus saran semoga game nya ditingkatkan disaat update
- text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu
    sinyal nya bagus tolong supercell perbaiki sinyal
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8307086614173228
      name: Accuracy
---

# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of filtering aspect span candidates.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model was trained within the context of a larger system for ABSA, which looks like so:

1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** id_core_news_trf
- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-polarity)
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label     | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect    | <ul><li>'pencarian lawan:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'game:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'bugnya:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li></ul>                                             |
| no aspect | <ul><li>'player trophy mahkotanya jaraknya:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'peleton akun perbedaan level:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'y coc cr:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8307   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import AbsaModel

# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
    "Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-aspect",
    "Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 29.9357 | 80  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 3834                  |
| aspect    | 1266                  |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step  | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1     | 0.2715        | -               |
| 0.0039 | 50    | 0.2364        | -               |
| 0.0078 | 100   | 0.1076        | -               |
| 0.0118 | 150   | 0.3431        | -               |
| 0.0157 | 200   | 0.2411        | -               |
| 0.0196 | 250   | 0.361         | -               |
| 0.0235 | 300   | 0.2227        | -               |
| 0.0275 | 350   | 0.2087        | -               |
| 0.0314 | 400   | 0.1956        | -               |
| 0.0353 | 450   | 0.2815        | -               |
| 0.0392 | 500   | 0.1844        | -               |
| 0.0431 | 550   | 0.2053        | -               |
| 0.0471 | 600   | 0.2884        | -               |
| 0.0510 | 650   | 0.1043        | -               |
| 0.0549 | 700   | 0.2074        | -               |
| 0.0588 | 750   | 0.1627        | -               |
| 0.0627 | 800   | 0.3           | -               |
| 0.0667 | 850   | 0.1658        | -               |
| 0.0706 | 900   | 0.1582        | -               |
| 0.0745 | 950   | 0.2692        | -               |
| 0.0784 | 1000  | 0.1823        | -               |
| 0.0824 | 1050  | 0.4098        | -               |
| 0.0863 | 1100  | 0.1992        | -               |
| 0.0902 | 1150  | 0.0793        | -               |
| 0.0941 | 1200  | 0.3924        | -               |
| 0.0980 | 1250  | 0.0339        | -               |
| 0.1020 | 1300  | 0.2236        | -               |
| 0.1059 | 1350  | 0.2262        | -               |
| 0.1098 | 1400  | 0.111         | -               |
| 0.1137 | 1450  | 0.0223        | -               |
| 0.1176 | 1500  | 0.3994        | -               |
| 0.1216 | 1550  | 0.0417        | -               |
| 0.1255 | 1600  | 0.3319        | -               |
| 0.1294 | 1650  | 0.3223        | -               |
| 0.1333 | 1700  | 0.2943        | -               |
| 0.1373 | 1750  | 0.1273        | -               |
| 0.1412 | 1800  | 0.2863        | -               |
| 0.1451 | 1850  | 0.0988        | -               |
| 0.1490 | 1900  | 0.1593        | -               |
| 0.1529 | 1950  | 0.2209        | -               |
| 0.1569 | 2000  | 0.5017        | -               |
| 0.1608 | 2050  | 0.1392        | -               |
| 0.1647 | 2100  | 0.1372        | -               |
| 0.1686 | 2150  | 0.3491        | -               |
| 0.1725 | 2200  | 0.2693        | -               |
| 0.1765 | 2250  | 0.1988        | -               |
| 0.1804 | 2300  | 0.2765        | -               |
| 0.1843 | 2350  | 0.238         | -               |
| 0.1882 | 2400  | 0.0577        | -               |
| 0.1922 | 2450  | 0.2253        | -               |
| 0.1961 | 2500  | 0.16          | -               |
| 0.2    | 2550  | 0.0262        | -               |
| 0.2039 | 2600  | 0.0099        | -               |
| 0.2078 | 2650  | 0.0132        | -               |
| 0.2118 | 2700  | 0.2356        | -               |
| 0.2157 | 2750  | 0.2975        | -               |
| 0.2196 | 2800  | 0.154         | -               |
| 0.2235 | 2850  | 0.0308        | -               |
| 0.2275 | 2900  | 0.0497        | -               |
| 0.2314 | 2950  | 0.0523        | -               |
| 0.2353 | 3000  | 0.158         | -               |
| 0.2392 | 3050  | 0.0473        | -               |
| 0.2431 | 3100  | 0.208         | -               |
| 0.2471 | 3150  | 0.2126        | -               |
| 0.2510 | 3200  | 0.081         | -               |
| 0.2549 | 3250  | 0.0134        | -               |
| 0.2588 | 3300  | 0.1107        | -               |
| 0.2627 | 3350  | 0.0249        | -               |
| 0.2667 | 3400  | 0.0259        | -               |
| 0.2706 | 3450  | 0.1008        | -               |
| 0.2745 | 3500  | 0.0335        | -               |
| 0.2784 | 3550  | 0.0119        | -               |
| 0.2824 | 3600  | 0.2982        | -               |
| 0.2863 | 3650  | 0.1516        | -               |
| 0.2902 | 3700  | 0.1217        | -               |
| 0.2941 | 3750  | 0.1558        | -               |
| 0.2980 | 3800  | 0.0359        | -               |
| 0.3020 | 3850  | 0.0215        | -               |
| 0.3059 | 3900  | 0.2906        | -               |
| 0.3098 | 3950  | 0.0599        | -               |
| 0.3137 | 4000  | 0.1528        | -               |
| 0.3176 | 4050  | 0.0144        | -               |
| 0.3216 | 4100  | 0.298         | -               |
| 0.3255 | 4150  | 0.0174        | -               |
| 0.3294 | 4200  | 0.0093        | -               |
| 0.3333 | 4250  | 0.0329        | -               |
| 0.3373 | 4300  | 0.1795        | -               |
| 0.3412 | 4350  | 0.0712        | -               |
| 0.3451 | 4400  | 0.3703        | -               |
| 0.3490 | 4450  | 0.0873        | -               |
| 0.3529 | 4500  | 0.3223        | -               |
| 0.3569 | 4550  | 0.0045        | -               |
| 0.3608 | 4600  | 0.2188        | -               |
| 0.3647 | 4650  | 0.0085        | -               |
| 0.3686 | 4700  | 0.2089        | -               |
| 0.3725 | 4750  | 0.0052        | -               |
| 0.3765 | 4800  | 0.1459        | -               |
| 0.3804 | 4850  | 0.0711        | -               |
| 0.3843 | 4900  | 0.4268        | -               |
| 0.3882 | 4950  | 0.1842        | -               |
| 0.3922 | 5000  | 0.1661        | -               |
| 0.3961 | 5050  | 0.1028        | -               |
| 0.4    | 5100  | 0.067         | -               |
| 0.4039 | 5150  | 0.1708        | -               |
| 0.4078 | 5200  | 0.1001        | -               |
| 0.4118 | 5250  | 0.065         | -               |
| 0.4157 | 5300  | 0.0279        | -               |
| 0.4196 | 5350  | 0.1101        | -               |
| 0.4235 | 5400  | 0.1923        | -               |
| 0.4275 | 5450  | 0.5491        | -               |
| 0.4314 | 5500  | 0.0726        | -               |
| 0.4353 | 5550  | 0.0085        | -               |
| 0.4392 | 5600  | 0.194         | -               |
| 0.4431 | 5650  | 0.2527        | -               |
| 0.4471 | 5700  | 0.7134        | -               |
| 0.4510 | 5750  | 0.4542        | -               |
| 0.4549 | 5800  | 0.2779        | -               |
| 0.4588 | 5850  | 0.1024        | -               |
| 0.4627 | 5900  | 0.2483        | -               |
| 0.4667 | 5950  | 0.0163        | -               |
| 0.4706 | 6000  | 0.0095        | -               |
| 0.4745 | 6050  | 0.2902        | -               |
| 0.4784 | 6100  | 0.0111        | -               |
| 0.4824 | 6150  | 0.0296        | -               |
| 0.4863 | 6200  | 0.3792        | -               |
| 0.4902 | 6250  | 0.4387        | -               |
| 0.4941 | 6300  | 0.1547        | -               |
| 0.4980 | 6350  | 0.0617        | -               |
| 0.5020 | 6400  | 0.1384        | -               |
| 0.5059 | 6450  | 0.0677        | -               |
| 0.5098 | 6500  | 0.0454        | -               |
| 0.5137 | 6550  | 0.0074        | -               |
| 0.5176 | 6600  | 0.1994        | -               |
| 0.5216 | 6650  | 0.0168        | -               |
| 0.5255 | 6700  | 0.0416        | -               |
| 0.5294 | 6750  | 0.1898        | -               |
| 0.5333 | 6800  | 0.0207        | -               |
| 0.5373 | 6850  | 0.1046        | -               |
| 0.5412 | 6900  | 0.1994        | -               |
| 0.5451 | 6950  | 0.0435        | -               |
| 0.5490 | 7000  | 0.0149        | -               |
| 0.5529 | 7050  | 0.0067        | -               |
| 0.5569 | 7100  | 0.0122        | -               |
| 0.5608 | 7150  | 0.2406        | -               |
| 0.5647 | 7200  | 0.4473        | -               |
| 0.5686 | 7250  | 0.0469        | -               |
| 0.5725 | 7300  | 0.1782        | -               |
| 0.5765 | 7350  | 0.3386        | -               |
| 0.5804 | 7400  | 0.2804        | -               |
| 0.5843 | 7450  | 0.0072        | -               |
| 0.5882 | 7500  | 0.0451        | -               |
| 0.5922 | 7550  | 0.0188        | -               |
| 0.5961 | 7600  | 0.01          | -               |
| 0.6    | 7650  | 0.0048        | -               |
| 0.6039 | 7700  | 0.2349        | -               |
| 0.6078 | 7750  | 0.2052        | -               |
| 0.6118 | 7800  | 0.0838        | -               |
| 0.6157 | 7850  | 0.3052        | -               |
| 0.6196 | 7900  | 0.3667        | -               |
| 0.6235 | 7950  | 0.0044        | -               |
| 0.6275 | 8000  | 0.3612        | -               |
| 0.6314 | 8050  | 0.2082        | -               |
| 0.6353 | 8100  | 0.3384        | -               |
| 0.6392 | 8150  | 0.022         | -               |
| 0.6431 | 8200  | 0.0764        | -               |
| 0.6471 | 8250  | 0.2879        | -               |
| 0.6510 | 8300  | 0.1827        | -               |
| 0.6549 | 8350  | 0.1104        | -               |
| 0.6588 | 8400  | 0.2096        | -               |
| 0.6627 | 8450  | 0.2103        | -               |
| 0.6667 | 8500  | 0.0742        | -               |
| 0.6706 | 8550  | 0.2186        | -               |
| 0.6745 | 8600  | 0.0109        | -               |
| 0.6784 | 8650  | 0.0326        | -               |
| 0.6824 | 8700  | 0.3056        | -               |
| 0.6863 | 8750  | 0.0941        | -               |
| 0.6902 | 8800  | 0.3731        | -               |
| 0.6941 | 8850  | 0.2185        | -               |
| 0.6980 | 8900  | 0.0228        | -               |
| 0.7020 | 8950  | 0.0141        | -               |
| 0.7059 | 9000  | 0.2242        | -               |
| 0.7098 | 9050  | 0.3303        | -               |
| 0.7137 | 9100  | 0.2383        | -               |
| 0.7176 | 9150  | 0.0026        | -               |
| 0.7216 | 9200  | 0.1718        | -               |
| 0.7255 | 9250  | 0.053         | -               |
| 0.7294 | 9300  | 0.0023        | -               |
| 0.7333 | 9350  | 0.221         | -               |
| 0.7373 | 9400  | 0.0021        | -               |
| 0.7412 | 9450  | 0.2333        | -               |
| 0.7451 | 9500  | 0.0565        | -               |
| 0.7490 | 9550  | 0.0271        | -               |
| 0.7529 | 9600  | 0.2156        | -               |
| 0.7569 | 9650  | 0.2349        | -               |
| 0.7608 | 9700  | 0.0047        | -               |
| 0.7647 | 9750  | 0.1273        | -               |
| 0.7686 | 9800  | 0.0139        | -               |
| 0.7725 | 9850  | 0.0231        | -               |
| 0.7765 | 9900  | 0.0048        | -               |
| 0.7804 | 9950  | 0.0022        | -               |
| 0.7843 | 10000 | 0.0026        | -               |
| 0.7882 | 10050 | 0.0223        | -               |
| 0.7922 | 10100 | 0.5488        | -               |
| 0.7961 | 10150 | 0.0281        | -               |
| 0.8    | 10200 | 0.0999        | -               |
| 0.8039 | 10250 | 0.2154        | -               |
| 0.8078 | 10300 | 0.0109        | -               |
| 0.8118 | 10350 | 0.0019        | -               |
| 0.8157 | 10400 | 0.1264        | -               |
| 0.8196 | 10450 | 0.0029        | -               |
| 0.8235 | 10500 | 0.3785        | -               |
| 0.8275 | 10550 | 0.0366        | -               |
| 0.8314 | 10600 | 0.0527        | -               |
| 0.8353 | 10650 | 0.2355        | -               |
| 0.8392 | 10700 | 0.0833        | -               |
| 0.8431 | 10750 | 0.1612        | -               |
| 0.8471 | 10800 | 0.0071        | -               |
| 0.8510 | 10850 | 0.1128        | -               |
| 0.8549 | 10900 | 0.2521        | -               |
| 0.8588 | 10950 | 0.0403        | -               |
| 0.8627 | 11000 | 0.2196        | -               |
| 0.8667 | 11050 | 0.1441        | -               |
| 0.8706 | 11100 | 0.0295        | -               |
| 0.8745 | 11150 | 0.0047        | -               |
| 0.8784 | 11200 | 0.3089        | -               |
| 0.8824 | 11250 | 0.1055        | -               |
| 0.8863 | 11300 | 0.0064        | -               |
| 0.8902 | 11350 | 0.2119        | -               |
| 0.8941 | 11400 | 0.2145        | -               |
| 0.8980 | 11450 | 0.0128        | -               |
| 0.9020 | 11500 | 0.0086        | -               |
| 0.9059 | 11550 | 0.1803        | -               |
| 0.9098 | 11600 | 0.2277        | -               |
| 0.9137 | 11650 | 0.0204        | -               |
| 0.9176 | 11700 | 0.0105        | -               |
| 0.9216 | 11750 | 0.005         | -               |
| 0.9255 | 11800 | 0.0099        | -               |
| 0.9294 | 11850 | 0.004         | -               |
| 0.9333 | 11900 | 0.1824        | -               |
| 0.9373 | 11950 | 0.0021        | -               |
| 0.9412 | 12000 | 0.2231        | -               |
| 0.9451 | 12050 | 0.0017        | -               |
| 0.9490 | 12100 | 0.0752        | -               |
| 0.9529 | 12150 | 0.0129        | -               |
| 0.9569 | 12200 | 0.1644        | -               |
| 0.9608 | 12250 | 0.0305        | -               |
| 0.9647 | 12300 | 0.0133        | -               |
| 0.9686 | 12350 | 0.0687        | -               |
| 0.9725 | 12400 | 0.0039        | -               |
| 0.9765 | 12450 | 0.1179        | -               |
| 0.9804 | 12500 | 0.1867        | -               |
| 0.9843 | 12550 | 0.0225        | -               |
| 0.9882 | 12600 | 0.1914        | -               |
| 0.9922 | 12650 | 0.0592        | -               |
| 0.9961 | 12700 | 0.0059        | -               |
| 1.0    | 12750 | 0.1016        | 0.2295          |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.5
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.19.2
- Tokenizers: 0.15.2

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->