Hugging Face's logo

Funnyworld1412
/
ABSA_bert-base_MiniLM-L6-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
Model card Files Community
Funnyworld1412 commited on
Commit
7afedae
•
1 Parent(s): 074e1cd

Add SetFit ABSA model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +473 -0
  3. config.json +26 -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 +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 +1,473 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: setfit
3
+ tags:
4
+ - setfit
5
+ - absa
6
+ - sentence-transformers
7
+ - text-classification
8
+ - generated_from_setfit_trainer
9
+ base_model: sentence-transformers/all-MiniLM-L6-v2
10
+ metrics:
11
+ - accuracy
12
+ widget:
13
+ - text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya
14
+ brawlstar ga
15
+ - text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan
16
+ game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon
17
+ perbaiki jaringan
18
+ - text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja
19
+ pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang
20
+ masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki
21
+ ya min klok grafik gameplay udah bagus
22
+ - text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan
23
+ jaringan bagus saran semoga game nya ditingkatkan disaat update
24
+ - text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu
25
+ sinyal nya bagus tolong supercell perbaiki sinyal
26
+ pipeline_tag: text-classification
27
+ inference: false
28
+ model-index:
29
+ - name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
30
+ results:
31
+ - task:
32
+ type: text-classification
33
+ name: Text Classification
34
+ dataset:
35
+ name: Unknown
36
+ type: unknown
37
+ split: test
38
+ metrics:
39
+ - type: accuracy
40
+ value: 0.8307086614173228
41
+ name: Accuracy
42
+ ---
43
+
44
+ # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
45
+
46
+ 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.
47
+
48
+ The model has been trained using an efficient few-shot learning technique that involves:
49
+
50
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
51
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
52
+
53
+ This model was trained within the context of a larger system for ABSA, which looks like so:
54
+
55
+ 1. Use a spaCy model to select possible aspect span candidates.
56
+ 2. **Use this SetFit model to filter these possible aspect span candidates.**
57
+ 3. Use a SetFit model to classify the filtered aspect span candidates.
58
+
59
+ ## Model Details
60
+
61
+ ### Model Description
62
+ - **Model Type:** SetFit
63
+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
64
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
65
+ - **spaCy Model:** id_core_news_trf
66
+ - **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect)
67
+ - **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_bert-base_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_bert-base_MiniLM-L6-polarity)
68
+ - **Maximum Sequence Length:** 256 tokens
69
+ - **Number of Classes:** 2 classes
70
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
71
+ <!-- - **Language:** Unknown -->
72
+ <!-- - **License:** Unknown -->
73
+
74
+ ### Model Sources
75
+
76
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
77
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
78
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
79
+
80
+ ### Model Labels
81
+ | Label | Examples |
82
+ |:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
83
+ | 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> |
84
+ | 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> |
85
+
86
+ ## Evaluation
87
+
88
+ ### Metrics
89
+ | Label | Accuracy |
90
+ |:--------|:---------|
91
+ | **all** | 0.8307 |
92
+
93
+ ## Uses
94
+
95
+ ### Direct Use for Inference
96
+
97
+ First install the SetFit library:
98
+
99
+ ```bash
100
+ pip install setfit
101
+ ```
102
+
103
+ Then you can load this model and run inference.
104
+
105
+ ```python
106
+ from setfit import AbsaModel
107
+
108
+ # Download from the 🤗 Hub
109
+ model = AbsaModel.from_pretrained(
110
+ "Funnyworld1412/ABSA_bert-base_MiniLM-L6-aspect",
111
+ "Funnyworld1412/ABSA_bert-base_MiniLM-L6-polarity",
112
+ )
113
+ # Run inference
114
+ preds = model("The food was great, but the venue is just way too busy.")
115
+ ```
116
+
117
+ <!--
118
+ ### Downstream Use
119
+
120
+ *List how someone could finetune this model on their own dataset.*
121
+ -->
122
+
123
+ <!--
124
+ ### Out-of-Scope Use
125
+
126
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
127
+ -->
128
+
129
+ <!--
130
+ ## Bias, Risks and Limitations
131
+
132
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
133
+ -->
134
+
135
+ <!--
136
+ ### Recommendations
137
+
138
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
139
+ -->
140
+
141
+ ## Training Details
142
+
143
+ ### Training Set Metrics
144
+ | Training set | Min | Median | Max |
145
+ |:-------------|:----|:--------|:----|
146
+ | Word count | 2 | 29.9357 | 80 |
147
+
148
+ | Label | Training Sample Count |
149
+ |:----------|:----------------------|
150
+ | no aspect | 3834 |
151
+ | aspect | 1266 |
152
+
153
+ ### Training Hyperparameters
154
+ - batch_size: (4, 4)
155
+ - num_epochs: (1, 1)
156
+ - max_steps: -1
157
+ - sampling_strategy: oversampling
158
+ - num_iterations: 5
159
+ - body_learning_rate: (2e-05, 1e-05)
160
+ - head_learning_rate: 0.01
161
+ - loss: CosineSimilarityLoss
162
+ - distance_metric: cosine_distance
163
+ - margin: 0.25
164
+ - end_to_end: False
165
+ - use_amp: False
166
+ - warmup_proportion: 0.1
167
+ - seed: 42
168
+ - eval_max_steps: -1
169
+ - load_best_model_at_end: False
170
+
171
+ ### Training Results
172
+ | Epoch | Step | Training Loss | Validation Loss |
173
+ |:------:|:-----:|:-------------:|:---------------:|
174
+ | 0.0001 | 1 | 0.2715 | - |
175
+ | 0.0039 | 50 | 0.2364 | - |
176
+ | 0.0078 | 100 | 0.1076 | - |
177
+ | 0.0118 | 150 | 0.3431 | - |
178
+ | 0.0157 | 200 | 0.2411 | - |
179
+ | 0.0196 | 250 | 0.361 | - |
180
+ | 0.0235 | 300 | 0.2227 | - |
181
+ | 0.0275 | 350 | 0.2087 | - |
182
+ | 0.0314 | 400 | 0.1956 | - |
183
+ | 0.0353 | 450 | 0.2815 | - |
184
+ | 0.0392 | 500 | 0.1844 | - |
185
+ | 0.0431 | 550 | 0.2053 | - |
186
+ | 0.0471 | 600 | 0.2884 | - |
187
+ | 0.0510 | 650 | 0.1043 | - |
188
+ | 0.0549 | 700 | 0.2074 | - |
189
+ | 0.0588 | 750 | 0.1627 | - |
190
+ | 0.0627 | 800 | 0.3 | - |
191
+ | 0.0667 | 850 | 0.1658 | - |
192
+ | 0.0706 | 900 | 0.1582 | - |
193
+ | 0.0745 | 950 | 0.2692 | - |
194
+ | 0.0784 | 1000 | 0.1823 | - |
195
+ | 0.0824 | 1050 | 0.4098 | - |
196
+ | 0.0863 | 1100 | 0.1992 | - |
197
+ | 0.0902 | 1150 | 0.0793 | - |
198
+ | 0.0941 | 1200 | 0.3924 | - |
199
+ | 0.0980 | 1250 | 0.0339 | - |
200
+ | 0.1020 | 1300 | 0.2236 | - |
201
+ | 0.1059 | 1350 | 0.2262 | - |
202
+ | 0.1098 | 1400 | 0.111 | - |
203
+ | 0.1137 | 1450 | 0.0223 | - |
204
+ | 0.1176 | 1500 | 0.3994 | - |
205
+ | 0.1216 | 1550 | 0.0417 | - |
206
+ | 0.1255 | 1600 | 0.3319 | - |
207
+ | 0.1294 | 1650 | 0.3223 | - |
208
+ | 0.1333 | 1700 | 0.2943 | - |
209
+ | 0.1373 | 1750 | 0.1273 | - |
210
+ | 0.1412 | 1800 | 0.2863 | - |
211
+ | 0.1451 | 1850 | 0.0988 | - |
212
+ | 0.1490 | 1900 | 0.1593 | - |
213
+ | 0.1529 | 1950 | 0.2209 | - |
214
+ | 0.1569 | 2000 | 0.5017 | - |
215
+ | 0.1608 | 2050 | 0.1392 | - |
216
+ | 0.1647 | 2100 | 0.1372 | - |
217
+ | 0.1686 | 2150 | 0.3491 | - |
218
+ | 0.1725 | 2200 | 0.2693 | - |
219
+ | 0.1765 | 2250 | 0.1988 | - |
220
+ | 0.1804 | 2300 | 0.2765 | - |
221
+ | 0.1843 | 2350 | 0.238 | - |
222
+ | 0.1882 | 2400 | 0.0577 | - |
223
+ | 0.1922 | 2450 | 0.2253 | - |
224
+ | 0.1961 | 2500 | 0.16 | - |
225
+ | 0.2 | 2550 | 0.0262 | - |
226
+ | 0.2039 | 2600 | 0.0099 | - |
227
+ | 0.2078 | 2650 | 0.0132 | - |
228
+ | 0.2118 | 2700 | 0.2356 | - |
229
+ | 0.2157 | 2750 | 0.2975 | - |
230
+ | 0.2196 | 2800 | 0.154 | - |
231
+ | 0.2235 | 2850 | 0.0308 | - |
232
+ | 0.2275 | 2900 | 0.0497 | - |
233
+ | 0.2314 | 2950 | 0.0523 | - |
234
+ | 0.2353 | 3000 | 0.158 | - |
235
+ | 0.2392 | 3050 | 0.0473 | - |
236
+ | 0.2431 | 3100 | 0.208 | - |
237
+ | 0.2471 | 3150 | 0.2126 | - |
238
+ | 0.2510 | 3200 | 0.081 | - |
239
+ | 0.2549 | 3250 | 0.0134 | - |
240
+ | 0.2588 | 3300 | 0.1107 | - |
241
+ | 0.2627 | 3350 | 0.0249 | - |
242
+ | 0.2667 | 3400 | 0.0259 | - |
243
+ | 0.2706 | 3450 | 0.1008 | - |
244
+ | 0.2745 | 3500 | 0.0335 | - |
245
+ | 0.2784 | 3550 | 0.0119 | - |
246
+ | 0.2824 | 3600 | 0.2982 | - |
247
+ | 0.2863 | 3650 | 0.1516 | - |
248
+ | 0.2902 | 3700 | 0.1217 | - |
249
+ | 0.2941 | 3750 | 0.1558 | - |
250
+ | 0.2980 | 3800 | 0.0359 | - |
251
+ | 0.3020 | 3850 | 0.0215 | - |
252
+ | 0.3059 | 3900 | 0.2906 | - |
253
+ | 0.3098 | 3950 | 0.0599 | - |
254
+ | 0.3137 | 4000 | 0.1528 | - |
255
+ | 0.3176 | 4050 | 0.0144 | - |
256
+ | 0.3216 | 4100 | 0.298 | - |
257
+ | 0.3255 | 4150 | 0.0174 | - |
258
+ | 0.3294 | 4200 | 0.0093 | - |
259
+ | 0.3333 | 4250 | 0.0329 | - |
260
+ | 0.3373 | 4300 | 0.1795 | - |
261
+ | 0.3412 | 4350 | 0.0712 | - |
262
+ | 0.3451 | 4400 | 0.3703 | - |
263
+ | 0.3490 | 4450 | 0.0873 | - |
264
+ | 0.3529 | 4500 | 0.3223 | - |
265
+ | 0.3569 | 4550 | 0.0045 | - |
266
+ | 0.3608 | 4600 | 0.2188 | - |
267
+ | 0.3647 | 4650 | 0.0085 | - |
268
+ | 0.3686 | 4700 | 0.2089 | - |
269
+ | 0.3725 | 4750 | 0.0052 | - |
270
+ | 0.3765 | 4800 | 0.1459 | - |
271
+ | 0.3804 | 4850 | 0.0711 | - |
272
+ | 0.3843 | 4900 | 0.4268 | - |
273
+ | 0.3882 | 4950 | 0.1842 | - |
274
+ | 0.3922 | 5000 | 0.1661 | - |
275
+ | 0.3961 | 5050 | 0.1028 | - |
276
+ | 0.4 | 5100 | 0.067 | - |
277
+ | 0.4039 | 5150 | 0.1708 | - |
278
+ | 0.4078 | 5200 | 0.1001 | - |
279
+ | 0.4118 | 5250 | 0.065 | - |
280
+ | 0.4157 | 5300 | 0.0279 | - |
281
+ | 0.4196 | 5350 | 0.1101 | - |
282
+ | 0.4235 | 5400 | 0.1923 | - |
283
+ | 0.4275 | 5450 | 0.5491 | - |
284
+ | 0.4314 | 5500 | 0.0726 | - |
285
+ | 0.4353 | 5550 | 0.0085 | - |
286
+ | 0.4392 | 5600 | 0.194 | - |
287
+ | 0.4431 | 5650 | 0.2527 | - |
288
+ | 0.4471 | 5700 | 0.7134 | - |
289
+ | 0.4510 | 5750 | 0.4542 | - |
290
+ | 0.4549 | 5800 | 0.2779 | - |
291
+ | 0.4588 | 5850 | 0.1024 | - |
292
+ | 0.4627 | 5900 | 0.2483 | - |
293
+ | 0.4667 | 5950 | 0.0163 | - |
294
+ | 0.4706 | 6000 | 0.0095 | - |
295
+ | 0.4745 | 6050 | 0.2902 | - |
296
+ | 0.4784 | 6100 | 0.0111 | - |
297
+ | 0.4824 | 6150 | 0.0296 | - |
298
+ | 0.4863 | 6200 | 0.3792 | - |
299
+ | 0.4902 | 6250 | 0.4387 | - |
300
+ | 0.4941 | 6300 | 0.1547 | - |
301
+ | 0.4980 | 6350 | 0.0617 | - |
302
+ | 0.5020 | 6400 | 0.1384 | - |
303
+ | 0.5059 | 6450 | 0.0677 | - |
304
+ | 0.5098 | 6500 | 0.0454 | - |
305
+ | 0.5137 | 6550 | 0.0074 | - |
306
+ | 0.5176 | 6600 | 0.1994 | - |
307
+ | 0.5216 | 6650 | 0.0168 | - |
308
+ | 0.5255 | 6700 | 0.0416 | - |
309
+ | 0.5294 | 6750 | 0.1898 | - |
310
+ | 0.5333 | 6800 | 0.0207 | - |
311
+ | 0.5373 | 6850 | 0.1046 | - |
312
+ | 0.5412 | 6900 | 0.1994 | - |
313
+ | 0.5451 | 6950 | 0.0435 | - |
314
+ | 0.5490 | 7000 | 0.0149 | - |
315
+ | 0.5529 | 7050 | 0.0067 | - |
316
+ | 0.5569 | 7100 | 0.0122 | - |
317
+ | 0.5608 | 7150 | 0.2406 | - |
318
+ | 0.5647 | 7200 | 0.4473 | - |
319
+ | 0.5686 | 7250 | 0.0469 | - |
320
+ | 0.5725 | 7300 | 0.1782 | - |
321
+ | 0.5765 | 7350 | 0.3386 | - |
322
+ | 0.5804 | 7400 | 0.2804 | - |
323
+ | 0.5843 | 7450 | 0.0072 | - |
324
+ | 0.5882 | 7500 | 0.0451 | - |
325
+ | 0.5922 | 7550 | 0.0188 | - |
326
+ | 0.5961 | 7600 | 0.01 | - |
327
+ | 0.6 | 7650 | 0.0048 | - |
328
+ | 0.6039 | 7700 | 0.2349 | - |
329
+ | 0.6078 | 7750 | 0.2052 | - |
330
+ | 0.6118 | 7800 | 0.0838 | - |
331
+ | 0.6157 | 7850 | 0.3052 | - |
332
+ | 0.6196 | 7900 | 0.3667 | - |
333
+ | 0.6235 | 7950 | 0.0044 | - |
334
+ | 0.6275 | 8000 | 0.3612 | - |
335
+ | 0.6314 | 8050 | 0.2082 | - |
336
+ | 0.6353 | 8100 | 0.3384 | - |
337
+ | 0.6392 | 8150 | 0.022 | - |
338
+ | 0.6431 | 8200 | 0.0764 | - |
339
+ | 0.6471 | 8250 | 0.2879 | - |
340
+ | 0.6510 | 8300 | 0.1827 | - |
341
+ | 0.6549 | 8350 | 0.1104 | - |
342
+ | 0.6588 | 8400 | 0.2096 | - |
343
+ | 0.6627 | 8450 | 0.2103 | - |
344
+ | 0.6667 | 8500 | 0.0742 | - |
345
+ | 0.6706 | 8550 | 0.2186 | - |
346
+ | 0.6745 | 8600 | 0.0109 | - |
347
+ | 0.6784 | 8650 | 0.0326 | - |
348
+ | 0.6824 | 8700 | 0.3056 | - |
349
+ | 0.6863 | 8750 | 0.0941 | - |
350
+ | 0.6902 | 8800 | 0.3731 | - |
351
+ | 0.6941 | 8850 | 0.2185 | - |
352
+ | 0.6980 | 8900 | 0.0228 | - |
353
+ | 0.7020 | 8950 | 0.0141 | - |
354
+ | 0.7059 | 9000 | 0.2242 | - |
355
+ | 0.7098 | 9050 | 0.3303 | - |
356
+ | 0.7137 | 9100 | 0.2383 | - |
357
+ | 0.7176 | 9150 | 0.0026 | - |
358
+ | 0.7216 | 9200 | 0.1718 | - |
359
+ | 0.7255 | 9250 | 0.053 | - |
360
+ | 0.7294 | 9300 | 0.0023 | - |
361
+ | 0.7333 | 9350 | 0.221 | - |
362
+ | 0.7373 | 9400 | 0.0021 | - |
363
+ | 0.7412 | 9450 | 0.2333 | - |
364
+ | 0.7451 | 9500 | 0.0565 | - |
365
+ | 0.7490 | 9550 | 0.0271 | - |
366
+ | 0.7529 | 9600 | 0.2156 | - |
367
+ | 0.7569 | 9650 | 0.2349 | - |
368
+ | 0.7608 | 9700 | 0.0047 | - |
369
+ | 0.7647 | 9750 | 0.1273 | - |
370
+ | 0.7686 | 9800 | 0.0139 | - |
371
+ | 0.7725 | 9850 | 0.0231 | - |
372
+ | 0.7765 | 9900 | 0.0048 | - |
373
+ | 0.7804 | 9950 | 0.0022 | - |
374
+ | 0.7843 | 10000 | 0.0026 | - |
375
+ | 0.7882 | 10050 | 0.0223 | - |
376
+ | 0.7922 | 10100 | 0.5488 | - |
377
+ | 0.7961 | 10150 | 0.0281 | - |
378
+ | 0.8 | 10200 | 0.0999 | - |
379
+ | 0.8039 | 10250 | 0.2154 | - |
380
+ | 0.8078 | 10300 | 0.0109 | - |
381
+ | 0.8118 | 10350 | 0.0019 | - |
382
+ | 0.8157 | 10400 | 0.1264 | - |
383
+ | 0.8196 | 10450 | 0.0029 | - |
384
+ | 0.8235 | 10500 | 0.3785 | - |
385
+ | 0.8275 | 10550 | 0.0366 | - |
386
+ | 0.8314 | 10600 | 0.0527 | - |
387
+ | 0.8353 | 10650 | 0.2355 | - |
388
+ | 0.8392 | 10700 | 0.0833 | - |
389
+ | 0.8431 | 10750 | 0.1612 | - |
390
+ | 0.8471 | 10800 | 0.0071 | - |
391
+ | 0.8510 | 10850 | 0.1128 | - |
392
+ | 0.8549 | 10900 | 0.2521 | - |
393
+ | 0.8588 | 10950 | 0.0403 | - |
394
+ | 0.8627 | 11000 | 0.2196 | - |
395
+ | 0.8667 | 11050 | 0.1441 | - |
396
+ | 0.8706 | 11100 | 0.0295 | - |
397
+ | 0.8745 | 11150 | 0.0047 | - |
398
+ | 0.8784 | 11200 | 0.3089 | - |
399
+ | 0.8824 | 11250 | 0.1055 | - |
400
+ | 0.8863 | 11300 | 0.0064 | - |
401
+ | 0.8902 | 11350 | 0.2119 | - |
402
+ | 0.8941 | 11400 | 0.2145 | - |
403
+ | 0.8980 | 11450 | 0.0128 | - |
404
+ | 0.9020 | 11500 | 0.0086 | - |
405
+ | 0.9059 | 11550 | 0.1803 | - |
406
+ | 0.9098 | 11600 | 0.2277 | - |
407
+ | 0.9137 | 11650 | 0.0204 | - |
408
+ | 0.9176 | 11700 | 0.0105 | - |
409
+ | 0.9216 | 11750 | 0.005 | - |
410
+ | 0.9255 | 11800 | 0.0099 | - |
411
+ | 0.9294 | 11850 | 0.004 | - |
412
+ | 0.9333 | 11900 | 0.1824 | - |
413
+ | 0.9373 | 11950 | 0.0021 | - |
414
+ | 0.9412 | 12000 | 0.2231 | - |
415
+ | 0.9451 | 12050 | 0.0017 | - |
416
+ | 0.9490 | 12100 | 0.0752 | - |
417
+ | 0.9529 | 12150 | 0.0129 | - |
418
+ | 0.9569 | 12200 | 0.1644 | - |
419
+ | 0.9608 | 12250 | 0.0305 | - |
420
+ | 0.9647 | 12300 | 0.0133 | - |
421
+ | 0.9686 | 12350 | 0.0687 | - |
422
+ | 0.9725 | 12400 | 0.0039 | - |
423
+ | 0.9765 | 12450 | 0.1179 | - |
424
+ | 0.9804 | 12500 | 0.1867 | - |
425
+ | 0.9843 | 12550 | 0.0225 | - |
426
+ | 0.9882 | 12600 | 0.1914 | - |
427
+ | 0.9922 | 12650 | 0.0592 | - |
428
+ | 0.9961 | 12700 | 0.0059 | - |
429
+ | 1.0 | 12750 | 0.1016 | 0.2295 |
430
+
431
+ ### Framework Versions
432
+ - Python: 3.10.13
433
+ - SetFit: 1.0.3
434
+ - Sentence Transformers: 3.0.1
435
+ - spaCy: 3.7.5
436
+ - Transformers: 4.36.2
437
+ - PyTorch: 2.1.2
438
+ - Datasets: 2.19.2
439
+ - Tokenizers: 0.15.2
440
+
441
+ ## Citation
442
+
443
+ ### BibTeX
444
+ ```bibtex
445
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
446
+ doi = {10.48550/ARXIV.2209.11055},
447
+ url = {https://arxiv.org/abs/2209.11055},
448
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
449
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
450
+ title = {Efficient Few-Shot Learning Without Prompts},
451
+ publisher = {arXiv},
452
+ year = {2022},
453
+ copyright = {Creative Commons Attribution 4.0 International}
454
+ }
455
+ ```
456
+
457
+ <!--
458
+ ## Glossary
459
+
460
+ *Clearly define terms in order to be accessible across audiences.*
461
+ -->
462
+
463
+ <!--
464
+ ## Model Card Authors
465
+
466
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
467
+ -->
468
+
469
+ <!--
470
+ ## Model Card Contact
471
+
472
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
473
+ -->
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": "4.36.2",
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": "3.0.1",
4
+ "transformers": "4.36.2",
5
+ "pytorch": "2.1.2"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "span_context": 0,
3
+ "spacy_model": "id_core_news_trf",
4
+ "labels": [
5
+ "no aspect",
6
+ "aspect"
7
+ ],
8
+ "normalize_embeddings": false
9
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1967209df9055c0c248fe4b66d5f618833ef5e893dff3a34a8e0e4177ee47d9
3
+ size 90864192
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cf44ab0efcf18c4c6b182add11ee48df8ddf8a4339d68587aa27d3a17c4d1422
3
+ size 3919
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