Hugging Face's logo

firqaaa
/
indo-setfit-bert-base-p3

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
firqaaa commited on
Commit
37ab8d6
•
1 Parent(s): c9e4f81

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +628 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +11 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -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 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
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
+ }
README.md ADDED
@@ -0,0 +1,628 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: setfit
3
+ tags:
4
+ - setfit
5
+ - sentence-transformers
6
+ - text-classification
7
+ - generated_from_setfit_trainer
8
+ metrics:
9
+ - accuracy
10
+ widget:
11
+ - text: Aku sudah lebih tua dan hidupku sangat berbeda. Aku bisa merasakan betapa
12
+ takjubnya aku pagi itu
13
+ - text: Saya merasa cukup href http kata-kata yang tak terucapkan disimpan di dalam
14
+ - text: Aku melihat ke dalam dompetku dan aku merasakan hawa dingin
15
+ - text: Aku menurunkan Erik dengan perasaan agak tidak puas dengan malam itu
16
+ - text: Aku bertanya-tanya apa yang siswa lain di kelasku rasakan ketika aku tidak
17
+ takut untuk memberikan jawaban di luar sana
18
+ pipeline_tag: text-classification
19
+ inference: true
20
+ base_model: firqaaa/indo-sentence-bert-base
21
+ model-index:
22
+ - name: SetFit with firqaaa/indo-sentence-bert-base
23
+ results:
24
+ - task:
25
+ type: text-classification
26
+ name: Text Classification
27
+ dataset:
28
+ name: Unknown
29
+ type: unknown
30
+ split: test
31
+ metrics:
32
+ - type: accuracy
33
+ value: 0.718
34
+ name: Accuracy
35
+ ---
36
+
37
+ # SetFit with firqaaa/indo-sentence-bert-base
38
+
39
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) 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.
40
+
41
+ The model has been trained using an efficient few-shot learning technique that involves:
42
+
43
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
44
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
45
+
46
+ ## Model Details
47
+
48
+ ### Model Description
49
+ - **Model Type:** SetFit
50
+ - **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
51
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
52
+ - **Maximum Sequence Length:** 512 tokens
53
+ - **Number of Classes:** 6 classes
54
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
55
+ <!-- - **Language:** Unknown -->
56
+ <!-- - **License:** Unknown -->
57
+
58
+ ### Model Sources
59
+
60
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
61
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
62
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
63
+
64
+ ### Model Labels
65
+ | Label | Examples |
66
+ |:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
67
+ | kesedihan | <ul><li>'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'</li><li>'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'</li><li>'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'</li></ul> |
68
+ | sukacita | <ul><li>'aku mungkin tidak merasa begitu keren'</li><li>'saya merasa baik-baik saja'</li><li>'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'</li></ul> |
69
+ | cinta | <ul><li>'aku merasa mencintaimu'</li><li>'aku akan merasa sangat nostalgia di usia yang begitu muda'</li><li>'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'</li></ul> |
70
+ | amarah | <ul><li>'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'</li><li>'aku merasa memberontak sedikit kesal gila terkurung'</li><li>'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'</li></ul> |
71
+ | takut | <ul><li>'aku merasa seperti diserang oleh landak titanium'</li><li>'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'</li><li>'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'</li></ul> |
72
+ | kejutan | <ul><li>'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'</li><li>'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'</li><li>'aku merasa penasaran, bersemangat dan tidak sabar'</li></ul> |
73
+
74
+ ## Evaluation
75
+
76
+ ### Metrics
77
+ | Label | Accuracy |
78
+ |:--------|:---------|
79
+ | **all** | 0.718 |
80
+
81
+ ## Uses
82
+
83
+ ### Direct Use for Inference
84
+
85
+ First install the SetFit library:
86
+
87
+ ```bash
88
+ pip install setfit
89
+ ```
90
+
91
+ Then you can load this model and run inference.
92
+
93
+ ```python
94
+ from setfit import SetFitModel
95
+
96
+ # Download from the 🤗 Hub
97
+ model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p3")
98
+ # Run inference
99
+ preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin")
100
+ ```
101
+
102
+ <!--
103
+ ### Downstream Use
104
+
105
+ *List how someone could finetune this model on their own dataset.*
106
+ -->
107
+
108
+ <!--
109
+ ### Out-of-Scope Use
110
+
111
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
112
+ -->
113
+
114
+ <!--
115
+ ## Bias, Risks and Limitations
116
+
117
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
118
+ -->
119
+
120
+ <!--
121
+ ### Recommendations
122
+
123
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
124
+ -->
125
+
126
+ ## Training Details
127
+
128
+ ### Training Set Metrics
129
+ | Training set | Min | Median | Max |
130
+ |:-------------|:----|:--------|:----|
131
+ | Word count | 2 | 16.7928 | 56 |
132
+
133
+ | Label | Training Sample Count |
134
+ |:----------|:----------------------|
135
+ | kesedihan | 300 |
136
+ | sukacita | 300 |
137
+ | cinta | 300 |
138
+ | amarah | 300 |
139
+ | takut | 300 |
140
+ | kejutan | 300 |
141
+
142
+ ### Training Hyperparameters
143
+ - batch_size: (128, 128)
144
+ - num_epochs: (1, 1)
145
+ - max_steps: -1
146
+ - sampling_strategy: oversampling
147
+ - body_learning_rate: (2e-05, 1e-05)
148
+ - head_learning_rate: 0.01
149
+ - loss: CosineSimilarityLoss
150
+ - distance_metric: cosine_distance
151
+ - margin: 0.25
152
+ - end_to_end: False
153
+ - use_amp: False
154
+ - warmup_proportion: 0.1
155
+ - seed: 42
156
+ - eval_max_steps: -1
157
+ - load_best_model_at_end: True
158
+
159
+ ### Training Results
160
+ | Epoch | Step | Training Loss | Validation Loss |
161
+ |:-------:|:---------:|:-------------:|:---------------:|
162
+ | 0.0000 | 1 | 0.2927 | - |
163
+ | 0.0024 | 50 | 0.2605 | - |
164
+ | 0.0047 | 100 | 0.2591 | - |
165
+ | 0.0071 | 150 | 0.2638 | - |
166
+ | 0.0095 | 200 | 0.245 | - |
167
+ | 0.0119 | 250 | 0.226 | - |
168
+ | 0.0142 | 300 | 0.222 | - |
169
+ | 0.0166 | 350 | 0.1968 | - |
170
+ | 0.0190 | 400 | 0.1703 | - |
171
+ | 0.0213 | 450 | 0.1703 | - |
172
+ | 0.0237 | 500 | 0.1587 | - |
173
+ | 0.0261 | 550 | 0.1087 | - |
174
+ | 0.0284 | 600 | 0.1203 | - |
175
+ | 0.0308 | 650 | 0.0844 | - |
176
+ | 0.0332 | 700 | 0.0696 | - |
177
+ | 0.0356 | 750 | 0.0606 | - |
178
+ | 0.0379 | 800 | 0.0333 | - |
179
+ | 0.0403 | 850 | 0.0453 | - |
180
+ | 0.0427 | 900 | 0.033 | - |
181
+ | 0.0450 | 950 | 0.0142 | - |
182
+ | 0.0474 | 1000 | 0.004 | - |
183
+ | 0.0498 | 1050 | 0.0097 | - |
184
+ | 0.0521 | 1100 | 0.0065 | - |
185
+ | 0.0545 | 1150 | 0.0081 | - |
186
+ | 0.0569 | 1200 | 0.0041 | - |
187
+ | 0.0593 | 1250 | 0.0044 | - |
188
+ | 0.0616 | 1300 | 0.0013 | - |
189
+ | 0.0640 | 1350 | 0.0024 | - |
190
+ | 0.0664 | 1400 | 0.001 | - |
191
+ | 0.0687 | 1450 | 0.0012 | - |
192
+ | 0.0711 | 1500 | 0.0013 | - |
193
+ | 0.0735 | 1550 | 0.0006 | - |
194
+ | 0.0759 | 1600 | 0.0033 | - |
195
+ | 0.0782 | 1650 | 0.0006 | - |
196
+ | 0.0806 | 1700 | 0.0013 | - |
197
+ | 0.0830 | 1750 | 0.0008 | - |
198
+ | 0.0853 | 1800 | 0.0006 | - |
199
+ | 0.0877 | 1850 | 0.0008 | - |
200
+ | 0.0901 | 1900 | 0.0004 | - |
201
+ | 0.0924 | 1950 | 0.0005 | - |
202
+ | 0.0948 | 2000 | 0.0004 | - |
203
+ | 0.0972 | 2050 | 0.0002 | - |
204
+ | 0.0996 | 2100 | 0.0002 | - |
205
+ | 0.1019 | 2150 | 0.0003 | - |
206
+ | 0.1043 | 2200 | 0.0006 | - |
207
+ | 0.1067 | 2250 | 0.0005 | - |
208
+ | 0.1090 | 2300 | 0.0003 | - |
209
+ | 0.1114 | 2350 | 0.0018 | - |
210
+ | 0.1138 | 2400 | 0.0003 | - |
211
+ | 0.1161 | 2450 | 0.0002 | - |
212
+ | 0.1185 | 2500 | 0.0018 | - |
213
+ | 0.1209 | 2550 | 0.0003 | - |
214
+ | 0.1233 | 2600 | 0.0008 | - |
215
+ | 0.1256 | 2650 | 0.0002 | - |
216
+ | 0.1280 | 2700 | 0.0007 | - |
217
+ | 0.1304 | 2750 | 0.006 | - |
218
+ | 0.1327 | 2800 | 0.0002 | - |
219
+ | 0.1351 | 2850 | 0.0001 | - |
220
+ | 0.1375 | 2900 | 0.0001 | - |
221
+ | 0.1399 | 2950 | 0.0001 | - |
222
+ | 0.1422 | 3000 | 0.0001 | - |
223
+ | 0.1446 | 3050 | 0.0001 | - |
224
+ | 0.1470 | 3100 | 0.0001 | - |
225
+ | 0.1493 | 3150 | 0.0001 | - |
226
+ | 0.1517 | 3200 | 0.0002 | - |
227
+ | 0.1541 | 3250 | 0.0003 | - |
228
+ | 0.1564 | 3300 | 0.0004 | - |
229
+ | 0.1588 | 3350 | 0.0001 | - |
230
+ | 0.1612 | 3400 | 0.0001 | - |
231
+ | 0.1636 | 3450 | 0.0014 | - |
232
+ | 0.1659 | 3500 | 0.0005 | - |
233
+ | 0.1683 | 3550 | 0.0003 | - |
234
+ | 0.1707 | 3600 | 0.0001 | - |
235
+ | 0.1730 | 3650 | 0.0001 | - |
236
+ | 0.1754 | 3700 | 0.0001 | - |
237
+ | 0.1778 | 3750 | 0.0001 | - |
238
+ | 0.1801 | 3800 | 0.0001 | - |
239
+ | 0.1825 | 3850 | 0.0001 | - |
240
+ | 0.1849 | 3900 | 0.0001 | - |
241
+ | 0.1873 | 3950 | 0.0001 | - |
242
+ | 0.1896 | 4000 | 0.0001 | - |
243
+ | 0.1920 | 4050 | 0.0001 | - |
244
+ | 0.1944 | 4100 | 0.0003 | - |
245
+ | 0.1967 | 4150 | 0.0006 | - |
246
+ | 0.1991 | 4200 | 0.0001 | - |
247
+ | 0.2015 | 4250 | 0.0 | - |
248
+ | 0.2038 | 4300 | 0.0 | - |
249
+ | 0.2062 | 4350 | 0.0001 | - |
250
+ | 0.2086 | 4400 | 0.0 | - |
251
+ | 0.2110 | 4450 | 0.0 | - |
252
+ | 0.2133 | 4500 | 0.0001 | - |
253
+ | 0.2157 | 4550 | 0.0002 | - |
254
+ | 0.2181 | 4600 | 0.0003 | - |
255
+ | 0.2204 | 4650 | 0.0018 | - |
256
+ | 0.2228 | 4700 | 0.0003 | - |
257
+ | 0.2252 | 4750 | 0.0145 | - |
258
+ | 0.2276 | 4800 | 0.0001 | - |
259
+ | 0.2299 | 4850 | 0.0006 | - |
260
+ | 0.2323 | 4900 | 0.0001 | - |
261
+ | 0.2347 | 4950 | 0.0007 | - |
262
+ | 0.2370 | 5000 | 0.0001 | - |
263
+ | 0.2394 | 5050 | 0.0 | - |
264
+ | 0.2418 | 5100 | 0.0 | - |
265
+ | 0.2441 | 5150 | 0.0001 | - |
266
+ | 0.2465 | 5200 | 0.0003 | - |
267
+ | 0.2489 | 5250 | 0.0 | - |
268
+ | 0.2513 | 5300 | 0.0 | - |
269
+ | 0.2536 | 5350 | 0.0 | - |
270
+ | 0.2560 | 5400 | 0.0 | - |
271
+ | 0.2584 | 5450 | 0.0004 | - |
272
+ | 0.2607 | 5500 | 0.0 | - |
273
+ | 0.2631 | 5550 | 0.0 | - |
274
+ | 0.2655 | 5600 | 0.0 | - |
275
+ | 0.2678 | 5650 | 0.0 | - |
276
+ | 0.2702 | 5700 | 0.0 | - |
277
+ | 0.2726 | 5750 | 0.0002 | - |
278
+ | 0.2750 | 5800 | 0.0 | - |
279
+ | 0.2773 | 5850 | 0.0 | - |
280
+ | 0.2797 | 5900 | 0.0 | - |
281
+ | 0.2821 | 5950 | 0.0 | - |
282
+ | 0.2844 | 6000 | 0.0 | - |
283
+ | 0.2868 | 6050 | 0.0 | - |
284
+ | 0.2892 | 6100 | 0.0 | - |
285
+ | 0.2916 | 6150 | 0.0 | - |
286
+ | 0.2939 | 6200 | 0.0 | - |
287
+ | 0.2963 | 6250 | 0.0 | - |
288
+ | 0.2987 | 6300 | 0.0001 | - |
289
+ | 0.3010 | 6350 | 0.0003 | - |
290
+ | 0.3034 | 6400 | 0.0048 | - |
291
+ | 0.3058 | 6450 | 0.0 | - |
292
+ | 0.3081 | 6500 | 0.0 | - |
293
+ | 0.3105 | 6550 | 0.0 | - |
294
+ | 0.3129 | 6600 | 0.0 | - |
295
+ | 0.3153 | 6650 | 0.0 | - |
296
+ | 0.3176 | 6700 | 0.0 | - |
297
+ | 0.3200 | 6750 | 0.0 | - |
298
+ | 0.3224 | 6800 | 0.0 | - |
299
+ | 0.3247 | 6850 | 0.0 | - |
300
+ | 0.3271 | 6900 | 0.0 | - |
301
+ | 0.3295 | 6950 | 0.0 | - |
302
+ | 0.3318 | 7000 | 0.0 | - |
303
+ | 0.3342 | 7050 | 0.0 | - |
304
+ | 0.3366 | 7100 | 0.0 | - |
305
+ | 0.3390 | 7150 | 0.0011 | - |
306
+ | 0.3413 | 7200 | 0.0002 | - |
307
+ | 0.3437 | 7250 | 0.0 | - |
308
+ | 0.3461 | 7300 | 0.0 | - |
309
+ | 0.3484 | 7350 | 0.0001 | - |
310
+ | 0.3508 | 7400 | 0.0001 | - |
311
+ | 0.3532 | 7450 | 0.0002 | - |
312
+ | 0.3556 | 7500 | 0.0 | - |
313
+ | 0.3579 | 7550 | 0.0 | - |
314
+ | 0.3603 | 7600 | 0.0 | - |
315
+ | 0.3627 | 7650 | 0.0 | - |
316
+ | 0.3650 | 7700 | 0.0 | - |
317
+ | 0.3674 | 7750 | 0.0 | - |
318
+ | 0.3698 | 7800 | 0.0001 | - |
319
+ | 0.3721 | 7850 | 0.0 | - |
320
+ | 0.3745 | 7900 | 0.0 | - |
321
+ | 0.3769 | 7950 | 0.0 | - |
322
+ | 0.3793 | 8000 | 0.0 | - |
323
+ | 0.3816 | 8050 | 0.0 | - |
324
+ | 0.3840 | 8100 | 0.0 | - |
325
+ | 0.3864 | 8150 | 0.0 | - |
326
+ | 0.3887 | 8200 | 0.0 | - |
327
+ | 0.3911 | 8250 | 0.0 | - |
328
+ | 0.3935 | 8300 | 0.0 | - |
329
+ | 0.3958 | 8350 | 0.0 | - |
330
+ | 0.3982 | 8400 | 0.0 | - |
331
+ | 0.4006 | 8450 | 0.0 | - |
332
+ | 0.4030 | 8500 | 0.0 | - |
333
+ | 0.4053 | 8550 | 0.0001 | - |
334
+ | 0.4077 | 8600 | 0.0001 | - |
335
+ | 0.4101 | 8650 | 0.0008 | - |
336
+ | 0.4124 | 8700 | 0.0001 | - |
337
+ | 0.4148 | 8750 | 0.0 | - |
338
+ | 0.4172 | 8800 | 0.0 | - |
339
+ | 0.4196 | 8850 | 0.0001 | - |
340
+ | 0.4219 | 8900 | 0.0 | - |
341
+ | 0.4243 | 8950 | 0.0 | - |
342
+ | 0.4267 | 9000 | 0.0 | - |
343
+ | 0.4290 | 9050 | 0.0 | - |
344
+ | 0.4314 | 9100 | 0.0 | - |
345
+ | 0.4338 | 9150 | 0.0 | - |
346
+ | 0.4361 | 9200 | 0.0 | - |
347
+ | 0.4385 | 9250 | 0.0 | - |
348
+ | 0.4409 | 9300 | 0.0 | - |
349
+ | 0.4433 | 9350 | 0.0 | - |
350
+ | 0.4456 | 9400 | 0.0 | - |
351
+ | 0.4480 | 9450 | 0.0 | - |
352
+ | 0.4504 | 9500 | 0.0 | - |
353
+ | 0.4527 | 9550 | 0.0 | - |
354
+ | 0.4551 | 9600 | 0.0 | - |
355
+ | 0.4575 | 9650 | 0.0 | - |
356
+ | 0.4598 | 9700 | 0.0 | - |
357
+ | 0.4622 | 9750 | 0.0001 | - |
358
+ | 0.4646 | 9800 | 0.0 | - |
359
+ | 0.4670 | 9850 | 0.0 | - |
360
+ | 0.4693 | 9900 | 0.0 | - |
361
+ | 0.4717 | 9950 | 0.0 | - |
362
+ | 0.4741 | 10000 | 0.0 | - |
363
+ | 0.4764 | 10050 | 0.0 | - |
364
+ | 0.4788 | 10100 | 0.0006 | - |
365
+ | 0.4812 | 10150 | 0.0 | - |
366
+ | 0.4835 | 10200 | 0.0 | - |
367
+ | 0.4859 | 10250 | 0.0 | - |
368
+ | 0.4883 | 10300 | 0.0 | - |
369
+ | 0.4907 | 10350 | 0.0 | - |
370
+ | 0.4930 | 10400 | 0.0 | - |
371
+ | 0.4954 | 10450 | 0.0 | - |
372
+ | 0.4978 | 10500 | 0.0 | - |
373
+ | 0.5001 | 10550 | 0.0 | - |
374
+ | 0.5025 | 10600 | 0.0 | - |
375
+ | 0.5049 | 10650 | 0.0 | - |
376
+ | 0.5073 | 10700 | 0.0 | - |
377
+ | 0.5096 | 10750 | 0.0 | - |
378
+ | 0.5120 | 10800 | 0.0 | - |
379
+ | 0.5144 | 10850 | 0.0 | - |
380
+ | 0.5167 | 10900 | 0.0 | - |
381
+ | 0.5191 | 10950 | 0.0 | - |
382
+ | 0.5215 | 11000 | 0.0 | - |
383
+ | 0.5238 | 11050 | 0.0 | - |
384
+ | 0.5262 | 11100 | 0.0 | - |
385
+ | 0.5286 | 11150 | 0.0 | - |
386
+ | 0.5310 | 11200 | 0.0 | - |
387
+ | 0.5333 | 11250 | 0.0 | - |
388
+ | 0.5357 | 11300 | 0.0 | - |
389
+ | 0.5381 | 11350 | 0.0 | - |
390
+ | 0.5404 | 11400 | 0.0 | - |
391
+ | 0.5428 | 11450 | 0.0 | - |
392
+ | 0.5452 | 11500 | 0.0 | - |
393
+ | 0.5475 | 11550 | 0.0 | - |
394
+ | 0.5499 | 11600 | 0.0 | - |
395
+ | 0.5523 | 11650 | 0.0001 | - |
396
+ | 0.5547 | 11700 | 0.0 | - |
397
+ | 0.5570 | 11750 | 0.0043 | - |
398
+ | 0.5594 | 11800 | 0.0 | - |
399
+ | 0.5618 | 11850 | 0.0 | - |
400
+ | 0.5641 | 11900 | 0.0 | - |
401
+ | 0.5665 | 11950 | 0.0 | - |
402
+ | 0.5689 | 12000 | 0.0 | - |
403
+ | 0.5713 | 12050 | 0.0 | - |
404
+ | 0.5736 | 12100 | 0.0 | - |
405
+ | 0.5760 | 12150 | 0.0 | - |
406
+ | 0.5784 | 12200 | 0.0 | - |
407
+ | 0.5807 | 12250 | 0.0029 | - |
408
+ | 0.5831 | 12300 | 0.0 | - |
409
+ | 0.5855 | 12350 | 0.0 | - |
410
+ | 0.5878 | 12400 | 0.0 | - |
411
+ | 0.5902 | 12450 | 0.0 | - |
412
+ | 0.5926 | 12500 | 0.0 | - |
413
+ | 0.5950 | 12550 | 0.0 | - |
414
+ | 0.5973 | 12600 | 0.0 | - |
415
+ | 0.5997 | 12650 | 0.0 | - |
416
+ | 0.6021 | 12700 | 0.0 | - |
417
+ | 0.6044 | 12750 | 0.0 | - |
418
+ | 0.6068 | 12800 | 0.0 | - |
419
+ | 0.6092 | 12850 | 0.0 | - |
420
+ | 0.6115 | 12900 | 0.0 | - |
421
+ | 0.6139 | 12950 | 0.0 | - |
422
+ | 0.6163 | 13000 | 0.0 | - |
423
+ | 0.6187 | 13050 | 0.0 | - |
424
+ | 0.6210 | 13100 | 0.0 | - |
425
+ | 0.6234 | 13150 | 0.0001 | - |
426
+ | 0.6258 | 13200 | 0.0 | - |
427
+ | 0.6281 | 13250 | 0.0 | - |
428
+ | 0.6305 | 13300 | 0.0 | - |
429
+ | 0.6329 | 13350 | 0.0 | - |
430
+ | 0.6353 | 13400 | 0.0001 | - |
431
+ | 0.6376 | 13450 | 0.0 | - |
432
+ | 0.6400 | 13500 | 0.0 | - |
433
+ | 0.6424 | 13550 | 0.0 | - |
434
+ | 0.6447 | 13600 | 0.0 | - |
435
+ | 0.6471 | 13650 | 0.0 | - |
436
+ | 0.6495 | 13700 | 0.0 | - |
437
+ | 0.6518 | 13750 | 0.0 | - |
438
+ | 0.6542 | 13800 | 0.0 | - |
439
+ | 0.6566 | 13850 | 0.0 | - |
440
+ | 0.6590 | 13900 | 0.0 | - |
441
+ | 0.6613 | 13950 | 0.0 | - |
442
+ | 0.6637 | 14000 | 0.0 | - |
443
+ | 0.6661 | 14050 | 0.0 | - |
444
+ | 0.6684 | 14100 | 0.0 | - |
445
+ | 0.6708 | 14150 | 0.0 | - |
446
+ | 0.6732 | 14200 | 0.0 | - |
447
+ | 0.6755 | 14250 | 0.0 | - |
448
+ | 0.6779 | 14300 | 0.0 | - |
449
+ | 0.6803 | 14350 | 0.0 | - |
450
+ | 0.6827 | 14400 | 0.0 | - |
451
+ | 0.6850 | 14450 | 0.0 | - |
452
+ | 0.6874 | 14500 | 0.0 | - |
453
+ | 0.6898 | 14550 | 0.0 | - |
454
+ | 0.6921 | 14600 | 0.0 | - |
455
+ | 0.6945 | 14650 | 0.0 | - |
456
+ | 0.6969 | 14700 | 0.0 | - |
457
+ | 0.6993 | 14750 | 0.0 | - |
458
+ | 0.7016 | 14800 | 0.0 | - |
459
+ | 0.7040 | 14850 | 0.0 | - |
460
+ | 0.7064 | 14900 | 0.0 | - |
461
+ | 0.7087 | 14950 | 0.0 | - |
462
+ | 0.7111 | 15000 | 0.0 | - |
463
+ | 0.7135 | 15050 | 0.0 | - |
464
+ | 0.7158 | 15100 | 0.0 | - |
465
+ | 0.7182 | 15150 | 0.0 | - |
466
+ | 0.7206 | 15200 | 0.0 | - |
467
+ | 0.7230 | 15250 | 0.0 | - |
468
+ | 0.7253 | 15300 | 0.0 | - |
469
+ | 0.7277 | 15350 | 0.0 | - |
470
+ | 0.7301 | 15400 | 0.0 | - |
471
+ | 0.7324 | 15450 | 0.0 | - |
472
+ | 0.7348 | 15500 | 0.0 | - |
473
+ | 0.7372 | 15550 | 0.0 | - |
474
+ | 0.7395 | 15600 | 0.0 | - |
475
+ | 0.7419 | 15650 | 0.0 | - |
476
+ | 0.7443 | 15700 | 0.0 | - |
477
+ | 0.7467 | 15750 | 0.0 | - |
478
+ | 0.7490 | 15800 | 0.0 | - |
479
+ | 0.7514 | 15850 | 0.0 | - |
480
+ | 0.7538 | 15900 | 0.0 | - |
481
+ | 0.7561 | 15950 | 0.0 | - |
482
+ | 0.7585 | 16000 | 0.0 | - |
483
+ | 0.7609 | 16050 | 0.0 | - |
484
+ | 0.7633 | 16100 | 0.0 | - |
485
+ | 0.7656 | 16150 | 0.0 | - |
486
+ | 0.7680 | 16200 | 0.0 | - |
487
+ | 0.7704 | 16250 | 0.0 | - |
488
+ | 0.7727 | 16300 | 0.0 | - |
489
+ | 0.7751 | 16350 | 0.0 | - |
490
+ | 0.7775 | 16400 | 0.0 | - |
491
+ | 0.7798 | 16450 | 0.0 | - |
492
+ | 0.7822 | 16500 | 0.0 | - |
493
+ | 0.7846 | 16550 | 0.0 | - |
494
+ | 0.7870 | 16600 | 0.0 | - |
495
+ | 0.7893 | 16650 | 0.0 | - |
496
+ | 0.7917 | 16700 | 0.0 | - |
497
+ | 0.7941 | 16750 | 0.0 | - |
498
+ | 0.7964 | 16800 | 0.0 | - |
499
+ | 0.7988 | 16850 | 0.0 | - |
500
+ | 0.8012 | 16900 | 0.0 | - |
501
+ | 0.8035 | 16950 | 0.0 | - |
502
+ | 0.8059 | 17000 | 0.0 | - |
503
+ | 0.8083 | 17050 | 0.0 | - |
504
+ | 0.8107 | 17100 | 0.0 | - |
505
+ | 0.8130 | 17150 | 0.0 | - |
506
+ | 0.8154 | 17200 | 0.0 | - |
507
+ | 0.8178 | 17250 | 0.0 | - |
508
+ | 0.8201 | 17300 | 0.0 | - |
509
+ | 0.8225 | 17350 | 0.0 | - |
510
+ | 0.8249 | 17400 | 0.0 | - |
511
+ | 0.8272 | 17450 | 0.0 | - |
512
+ | 0.8296 | 17500 | 0.0 | - |
513
+ | 0.8320 | 17550 | 0.0 | - |
514
+ | 0.8344 | 17600 | 0.0 | - |
515
+ | 0.8367 | 17650 | 0.0 | - |
516
+ | 0.8391 | 17700 | 0.0 | - |
517
+ | 0.8415 | 17750 | 0.0 | - |
518
+ | 0.8438 | 17800 | 0.0 | - |
519
+ | 0.8462 | 17850 | 0.0 | - |
520
+ | 0.8486 | 17900 | 0.0 | - |
521
+ | 0.8510 | 17950 | 0.0 | - |
522
+ | 0.8533 | 18000 | 0.0 | - |
523
+ | 0.8557 | 18050 | 0.0 | - |
524
+ | 0.8581 | 18100 | 0.0 | - |
525
+ | 0.8604 | 18150 | 0.0 | - |
526
+ | 0.8628 | 18200 | 0.0 | - |
527
+ | 0.8652 | 18250 | 0.0 | - |
528
+ | 0.8675 | 18300 | 0.0 | - |
529
+ | 0.8699 | 18350 | 0.0 | - |
530
+ | 0.8723 | 18400 | 0.0 | - |
531
+ | 0.8747 | 18450 | 0.0 | - |
532
+ | 0.8770 | 18500 | 0.0 | - |
533
+ | 0.8794 | 18550 | 0.0 | - |
534
+ | 0.8818 | 18600 | 0.0 | - |
535
+ | 0.8841 | 18650 | 0.0 | - |
536
+ | 0.8865 | 18700 | 0.0 | - |
537
+ | 0.8889 | 18750 | 0.0 | - |
538
+ | 0.8912 | 18800 | 0.0 | - |
539
+ | 0.8936 | 18850 | 0.0 | - |
540
+ | 0.8960 | 18900 | 0.0 | - |
541
+ | 0.8984 | 18950 | 0.0 | - |
542
+ | 0.9007 | 19000 | 0.0 | - |
543
+ | 0.9031 | 19050 | 0.0 | - |
544
+ | 0.9055 | 19100 | 0.0 | - |
545
+ | 0.9078 | 19150 | 0.0 | - |
546
+ | 0.9102 | 19200 | 0.0 | - |
547
+ | 0.9126 | 19250 | 0.0 | - |
548
+ | 0.9150 | 19300 | 0.0 | - |
549
+ | 0.9173 | 19350 | 0.0 | - |
550
+ | 0.9197 | 19400 | 0.0 | - |
551
+ | 0.9221 | 19450 | 0.0 | - |
552
+ | 0.9244 | 19500 | 0.0 | - |
553
+ | 0.9268 | 19550 | 0.0 | - |
554
+ | 0.9292 | 19600 | 0.0 | - |
555
+ | 0.9315 | 19650 | 0.0 | - |
556
+ | 0.9339 | 19700 | 0.0 | - |
557
+ | 0.9363 | 19750 | 0.0 | - |
558
+ | 0.9387 | 19800 | 0.0 | - |
559
+ | 0.9410 | 19850 | 0.0 | - |
560
+ | 0.9434 | 19900 | 0.0 | - |
561
+ | 0.9458 | 19950 | 0.0 | - |
562
+ | 0.9481 | 20000 | 0.0 | - |
563
+ | 0.9505 | 20050 | 0.0 | - |
564
+ | 0.9529 | 20100 | 0.0 | - |
565
+ | 0.9552 | 20150 | 0.0 | - |
566
+ | 0.9576 | 20200 | 0.0 | - |
567
+ | 0.9600 | 20250 | 0.0 | - |
568
+ | 0.9624 | 20300 | 0.0 | - |
569
+ | 0.9647 | 20350 | 0.0 | - |
570
+ | 0.9671 | 20400 | 0.0 | - |
571
+ | 0.9695 | 20450 | 0.0 | - |
572
+ | 0.9718 | 20500 | 0.0 | - |
573
+ | 0.9742 | 20550 | 0.0 | - |
574
+ | 0.9766 | 20600 | 0.0 | - |
575
+ | 0.9790 | 20650 | 0.0 | - |
576
+ | 0.9813 | 20700 | 0.0 | - |
577
+ | 0.9837 | 20750 | 0.0 | - |
578
+ | 0.9861 | 20800 | 0.0 | - |
579
+ | 0.9884 | 20850 | 0.0 | - |
580
+ | 0.9908 | 20900 | 0.0 | - |
581
+ | 0.9932 | 20950 | 0.0 | - |
582
+ | 0.9955 | 21000 | 0.0 | - |
583
+ | 0.9979 | 21050 | 0.0 | - |
584
+ | **1.0** | **21094** | **-** | **0.2251** |
585
+
586
+ * The bold row denotes the saved checkpoint.
587
+ ### Framework Versions
588
+ - Python: 3.10.13
589
+ - SetFit: 1.0.3
590
+ - Sentence Transformers: 2.2.2
591
+ - Transformers: 4.36.2
592
+ - PyTorch: 2.1.2+cu121
593
+ - Datasets: 2.16.1
594
+ - Tokenizers: 0.15.0
595
+
596
+ ## Citation
597
+
598
+ ### BibTeX
599
+ ```bibtex
600
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
601
+ doi = {10.48550/ARXIV.2209.11055},
602
+ url = {https://arxiv.org/abs/2209.11055},
603
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
604
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
605
+ title = {Efficient Few-Shot Learning Without Prompts},
606
+ publisher = {arXiv},
607
+ year = {2022},
608
+ copyright = {Creative Commons Attribution 4.0 International}
609
+ }
610
+ ```
611
+
612
+ <!--
613
+ ## Glossary
614
+
615
+ *Clearly define terms in order to be accessible across audiences.*
616
+ -->
617
+
618
+ <!--
619
+ ## Model Card Authors
620
+
621
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
622
+ -->
623
+
624
+ <!--
625
+ ## Model Card Contact
626
+
627
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
628
+ -->
config.json ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "checkpoints/step_21094/",
3
+ "_num_labels": 5,
4
+ "architectures": [
5
+ "BertModel"
6
+ ],
7
+ "attention_probs_dropout_prob": 0.1,
8
+ "classifier_dropout": null,
9
+ "directionality": "bidi",
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 768,
13
+ "id2label": {
14
+ "0": "LABEL_0",
15
+ "1": "LABEL_1",
16
+ "2": "LABEL_2",
17
+ "3": "LABEL_3",
18
+ "4": "LABEL_4"
19
+ },
20
+ "initializer_range": 0.02,
21
+ "intermediate_size": 3072,
22
+ "label2id": {
23
+ "LABEL_0": 0,
24
+ "LABEL_1": 1,
25
+ "LABEL_2": 2,
26
+ "LABEL_3": 3,
27
+ "LABEL_4": 4
28
+ },
29
+ "layer_norm_eps": 1e-12,
30
+ "max_position_embeddings": 512,
31
+ "model_type": "bert",
32
+ "num_attention_heads": 12,
33
+ "num_hidden_layers": 12,
34
+ "output_past": true,
35
+ "pad_token_id": 0,
36
+ "pooler_fc_size": 768,
37
+ "pooler_num_attention_heads": 12,
38
+ "pooler_num_fc_layers": 3,
39
+ "pooler_size_per_head": 128,
40
+ "pooler_type": "first_token_transform",
41
+ "position_embedding_type": "absolute",
42
+ "torch_dtype": "float32",
43
+ "transformers_version": "4.36.2",
44
+ "type_vocab_size": 2,
45
+ "use_cache": true,
46
+ "vocab_size": 50000
47
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.2.2",
4
+ "transformers": "4.20.1",
5
+ "pytorch": "1.11.0"
6
+ }
7
+ }
config_setfit.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "labels": [
3
+ "kesedihan",
4
+ "sukacita",
5
+ "cinta",
6
+ "amarah",
7
+ "takut",
8
+ "kejutan"
9
+ ],
10
+ "normalize_embeddings": false
11
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9746f47e90cf0e3c0fe3ff272208e989c11b1b86906723ad2e2c0eab0fdb3eda
3
+ size 497787752
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6916804f1dc446c169e25c5ffa25b10f8ff68c40e1ffe28a2f2ca5eaafff4085
3
+ size 37799
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
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
+ "1": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "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": 512,
50
+ "model_max_length": 1000000000000000019884624838656,
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