Add SetFit ABSA model
Browse files- 1_Pooling/config.json +10 -0
- README.md +899 -0
- config.json +47 -0
- config_sentence_transformers.json +9 -0
- config_setfit.json +9 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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1 |
+
---
|
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+
library_name: setfit
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+
tags:
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- setfit
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+
- absa
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+
- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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metrics:
|
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- accuracy
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+
widget:
|
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+
- text: sashimi:Kami berbagi sebotol sake, pesanan edamame, dan dia makan sesepiring
|
13 |
+
sushi sementara saya makan sashimi.
|
14 |
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- text: kelompok:tempat agak kecil tapi saya kira jika mereka tidak terlalu sibuk
|
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mungkin bisa memuat kelompok atau anak-anak.
|
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- text: Suan:Lokasinya yang bagus dan fakta bahwa Hutner College dekat serta harga
|
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sangat masuk akal, membuat siswa kembali ke Suan lagi dan lagi.
|
18 |
+
- text: rapido:Di sebelah kanan saya, nyonya rumah berdiri di dekat seorang busboy
|
19 |
+
dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang
|
20 |
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meja untuk enam orang.
|
21 |
+
- text: hidangan:Jangan bersantap di Tamarind untuk hidangan vegetarian, mereka tidak
|
22 |
+
setara dengan pilihan non-sayuran.
|
23 |
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pipeline_tag: text-classification
|
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inference: false
|
25 |
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model-index:
|
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- name: SetFit Aspect Model
|
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results:
|
28 |
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- task:
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type: text-classification
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name: Text Classification
|
31 |
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dataset:
|
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name: Unknown
|
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type: unknown
|
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split: test
|
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+
metrics:
|
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- type: accuracy
|
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value: 0.7836879432624113
|
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name: Accuracy
|
39 |
+
---
|
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+
|
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+
# SetFit Aspect Model
|
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+
|
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+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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.
|
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+
|
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+
The model has been trained using an efficient few-shot learning technique that involves:
|
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|
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
|
48 |
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
|
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+
|
50 |
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This model was trained within the context of a larger system for ABSA, which looks like so:
|
51 |
+
|
52 |
+
1. Use a spaCy model to select possible aspect span candidates.
|
53 |
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2. **Use this SetFit model to filter these possible aspect span candidates.**
|
54 |
+
3. Use a SetFit model to classify the filtered aspect span candidates.
|
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+
|
56 |
+
## Model Details
|
57 |
+
|
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+
### Model Description
|
59 |
+
- **Model Type:** SetFit
|
60 |
+
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
|
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
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- **spaCy Model:** id_core_news_trf
|
63 |
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- **SetFitABSA Aspect Model:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect)
|
64 |
+
- **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity)
|
65 |
+
- **Maximum Sequence Length:** 512 tokens
|
66 |
+
- **Number of Classes:** 2 classes
|
67 |
+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
68 |
+
<!-- - **Language:** Unknown -->
|
69 |
+
<!-- - **License:** Unknown -->
|
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+
|
71 |
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### Model Sources
|
72 |
+
|
73 |
+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
74 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
75 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
76 |
+
|
77 |
+
### Model Labels
|
78 |
+
| Label | Examples |
|
79 |
+
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
80 |
+
| aspect | <ul><li>'makanannya:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'makanannya:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'dapur:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li></ul> |
|
81 |
+
| no aspect | <ul><li>'faktor penebusan:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'atas:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'kekurangan Teodora:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li></ul> |
|
82 |
+
|
83 |
+
## Evaluation
|
84 |
+
|
85 |
+
### Metrics
|
86 |
+
| Label | Accuracy |
|
87 |
+
|:--------|:---------|
|
88 |
+
| **all** | 0.7837 |
|
89 |
+
|
90 |
+
## Uses
|
91 |
+
|
92 |
+
### Direct Use for Inference
|
93 |
+
|
94 |
+
First install the SetFit library:
|
95 |
+
|
96 |
+
```bash
|
97 |
+
pip install setfit
|
98 |
+
```
|
99 |
+
|
100 |
+
Then you can load this model and run inference.
|
101 |
+
|
102 |
+
```python
|
103 |
+
from setfit import AbsaModel
|
104 |
+
|
105 |
+
# Download from the 🤗 Hub
|
106 |
+
model = AbsaModel.from_pretrained(
|
107 |
+
"zeroix07/indo-setfit-absa-model-aspect",
|
108 |
+
"zeroix07/indo-setfit-absa-model-polarity",
|
109 |
+
)
|
110 |
+
# Run inference
|
111 |
+
preds = model("The food was great, but the venue is just way too busy.")
|
112 |
+
```
|
113 |
+
|
114 |
+
<!--
|
115 |
+
### Downstream Use
|
116 |
+
|
117 |
+
*List how someone could finetune this model on their own dataset.*
|
118 |
+
-->
|
119 |
+
|
120 |
+
<!--
|
121 |
+
### Out-of-Scope Use
|
122 |
+
|
123 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
124 |
+
-->
|
125 |
+
|
126 |
+
<!--
|
127 |
+
## Bias, Risks and Limitations
|
128 |
+
|
129 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
130 |
+
-->
|
131 |
+
|
132 |
+
<!--
|
133 |
+
### Recommendations
|
134 |
+
|
135 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
136 |
+
-->
|
137 |
+
|
138 |
+
## Training Details
|
139 |
+
|
140 |
+
### Training Set Metrics
|
141 |
+
| Training set | Min | Median | Max |
|
142 |
+
|:-------------|:----|:--------|:----|
|
143 |
+
| Word count | 2 | 17.4396 | 40 |
|
144 |
+
|
145 |
+
| Label | Training Sample Count |
|
146 |
+
|:----------|:----------------------|
|
147 |
+
| no aspect | 415 |
|
148 |
+
| aspect | 181 |
|
149 |
+
|
150 |
+
### Training Hyperparameters
|
151 |
+
- batch_size: (6, 6)
|
152 |
+
- num_epochs: (1, 16)
|
153 |
+
- max_steps: -1
|
154 |
+
- sampling_strategy: oversampling
|
155 |
+
- body_learning_rate: (2e-05, 1e-05)
|
156 |
+
- head_learning_rate: 0.01
|
157 |
+
- loss: CosineSimilarityLoss
|
158 |
+
- distance_metric: cosine_distance
|
159 |
+
- margin: 0.25
|
160 |
+
- end_to_end: False
|
161 |
+
- use_amp: True
|
162 |
+
- warmup_proportion: 0.1
|
163 |
+
- seed: 42
|
164 |
+
- eval_max_steps: -1
|
165 |
+
- load_best_model_at_end: False
|
166 |
+
|
167 |
+
### Training Results
|
168 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
169 |
+
|:------:|:-----:|:-------------:|:---------------:|
|
170 |
+
| 0.0000 | 1 | 0.2509 | - |
|
171 |
+
| 0.0015 | 50 | 0.1002 | - |
|
172 |
+
| 0.0029 | 100 | 0.2166 | - |
|
173 |
+
| 0.0044 | 150 | 0.1083 | - |
|
174 |
+
| 0.0058 | 200 | 0.2008 | - |
|
175 |
+
| 0.0073 | 250 | 0.2292 | - |
|
176 |
+
| 0.0088 | 300 | 0.1745 | - |
|
177 |
+
| 0.0102 | 350 | 0.207 | - |
|
178 |
+
| 0.0117 | 400 | 0.0432 | - |
|
179 |
+
| 0.0131 | 450 | 0.0122 | - |
|
180 |
+
| 0.0146 | 500 | 0.0318 | - |
|
181 |
+
| 0.0161 | 550 | 0.0037 | - |
|
182 |
+
| 0.0175 | 600 | 0.0065 | - |
|
183 |
+
| 0.0190 | 650 | 0.0401 | - |
|
184 |
+
| 0.0204 | 700 | 0.0015 | - |
|
185 |
+
| 0.0219 | 750 | 0.0043 | - |
|
186 |
+
| 0.0233 | 800 | 0.0968 | - |
|
187 |
+
| 0.0248 | 850 | 0.1695 | - |
|
188 |
+
| 0.0263 | 900 | 0.0037 | - |
|
189 |
+
| 0.0277 | 950 | 0.001 | - |
|
190 |
+
| 0.0292 | 1000 | 0.0041 | - |
|
191 |
+
| 0.0306 | 1050 | 0.0009 | - |
|
192 |
+
| 0.0321 | 1100 | 0.0025 | - |
|
193 |
+
| 0.0336 | 1150 | 0.0015 | - |
|
194 |
+
| 0.0350 | 1200 | 0.0763 | - |
|
195 |
+
| 0.0365 | 1250 | 0.2008 | - |
|
196 |
+
| 0.0379 | 1300 | 0.0015 | - |
|
197 |
+
| 0.0394 | 1350 | 0.0766 | - |
|
198 |
+
| 0.0409 | 1400 | 0.2491 | - |
|
199 |
+
| 0.0423 | 1450 | 0.1411 | - |
|
200 |
+
| 0.0438 | 1500 | 0.0007 | - |
|
201 |
+
| 0.0452 | 1550 | 0.0057 | - |
|
202 |
+
| 0.0467 | 1600 | 0.0007 | - |
|
203 |
+
| 0.0482 | 1650 | 0.1603 | - |
|
204 |
+
| 0.0496 | 1700 | 0.0006 | - |
|
205 |
+
| 0.0511 | 1750 | 0.0019 | - |
|
206 |
+
| 0.0525 | 1800 | 0.0005 | - |
|
207 |
+
| 0.0540 | 1850 | 0.0005 | - |
|
208 |
+
| 0.0555 | 1900 | 0.2637 | - |
|
209 |
+
| 0.0569 | 1950 | 0.0011 | - |
|
210 |
+
| 0.0584 | 2000 | 0.0008 | - |
|
211 |
+
| 0.0598 | 2050 | 0.0017 | - |
|
212 |
+
| 0.0613 | 2100 | 0.0005 | - |
|
213 |
+
| 0.0627 | 2150 | 0.0002 | - |
|
214 |
+
| 0.0642 | 2200 | 0.0766 | - |
|
215 |
+
| 0.0657 | 2250 | 0.0001 | - |
|
216 |
+
| 0.0671 | 2300 | 0.0002 | - |
|
217 |
+
| 0.0686 | 2350 | 0.0023 | - |
|
218 |
+
| 0.0700 | 2400 | 0.0001 | - |
|
219 |
+
| 0.0715 | 2450 | 0.0122 | - |
|
220 |
+
| 0.0730 | 2500 | 0.001 | - |
|
221 |
+
| 0.0744 | 2550 | 0.0006 | - |
|
222 |
+
| 0.0759 | 2600 | 0.0056 | - |
|
223 |
+
| 0.0773 | 2650 | 0.0022 | - |
|
224 |
+
| 0.0788 | 2700 | 0.0002 | - |
|
225 |
+
| 0.0803 | 2750 | 0.0213 | - |
|
226 |
+
| 0.0817 | 2800 | 0.047 | - |
|
227 |
+
| 0.0832 | 2850 | 0.0002 | - |
|
228 |
+
| 0.0846 | 2900 | 0.0135 | - |
|
229 |
+
| 0.0861 | 2950 | 0.0473 | - |
|
230 |
+
| 0.0876 | 3000 | 0.0003 | - |
|
231 |
+
| 0.0890 | 3050 | 0.0078 | - |
|
232 |
+
| 0.0905 | 3100 | 0.0001 | - |
|
233 |
+
| 0.0919 | 3150 | 0.0002 | - |
|
234 |
+
| 0.0934 | 3200 | 0.008 | - |
|
235 |
+
| 0.0949 | 3250 | 0.0005 | - |
|
236 |
+
| 0.0963 | 3300 | 0.0002 | - |
|
237 |
+
| 0.0978 | 3350 | 0.0062 | - |
|
238 |
+
| 0.0992 | 3400 | 0.0002 | - |
|
239 |
+
| 0.1007 | 3450 | 0.0002 | - |
|
240 |
+
| 0.1021 | 3500 | 0.0007 | - |
|
241 |
+
| 0.1036 | 3550 | 0.0017 | - |
|
242 |
+
| 0.1051 | 3600 | 0.1652 | - |
|
243 |
+
| 0.1065 | 3650 | 0.0011 | - |
|
244 |
+
| 0.1080 | 3700 | 0.0 | - |
|
245 |
+
| 0.1094 | 3750 | 0.0003 | - |
|
246 |
+
| 0.1109 | 3800 | 0.0007 | - |
|
247 |
+
| 0.1124 | 3850 | 0.0006 | - |
|
248 |
+
| 0.1138 | 3900 | 0.0001 | - |
|
249 |
+
| 0.1153 | 3950 | 0.002 | - |
|
250 |
+
| 0.1167 | 4000 | 0.0001 | - |
|
251 |
+
| 0.1182 | 4050 | 0.0004 | - |
|
252 |
+
| 0.1197 | 4100 | 0.0003 | - |
|
253 |
+
| 0.1211 | 4150 | 0.0295 | - |
|
254 |
+
| 0.1226 | 4200 | 0.0012 | - |
|
255 |
+
| 0.1240 | 4250 | 0.0004 | - |
|
256 |
+
| 0.1255 | 4300 | 0.0003 | - |
|
257 |
+
| 0.1270 | 4350 | 0.0364 | - |
|
258 |
+
| 0.1284 | 4400 | 0.042 | - |
|
259 |
+
| 0.1299 | 4450 | 0.0 | - |
|
260 |
+
| 0.1313 | 4500 | 0.0 | - |
|
261 |
+
| 0.1328 | 4550 | 0.0001 | - |
|
262 |
+
| 0.1343 | 4600 | 0.0159 | - |
|
263 |
+
| 0.1357 | 4650 | 0.0001 | - |
|
264 |
+
| 0.1372 | 4700 | 0.0 | - |
|
265 |
+
| 0.1386 | 4750 | 0.0004 | - |
|
266 |
+
| 0.1401 | 4800 | 0.0409 | - |
|
267 |
+
| 0.1415 | 4850 | 0.0411 | - |
|
268 |
+
| 0.1430 | 4900 | 0.0001 | - |
|
269 |
+
| 0.1445 | 4950 | 0.0002 | - |
|
270 |
+
| 0.1459 | 5000 | 0.0 | - |
|
271 |
+
| 0.1474 | 5050 | 0.1251 | - |
|
272 |
+
| 0.1488 | 5100 | 0.0 | - |
|
273 |
+
| 0.1503 | 5150 | 0.0001 | - |
|
274 |
+
| 0.1518 | 5200 | 0.0 | - |
|
275 |
+
| 0.1532 | 5250 | 0.0 | - |
|
276 |
+
| 0.1547 | 5300 | 0.0466 | - |
|
277 |
+
| 0.1561 | 5350 | 0.0 | - |
|
278 |
+
| 0.1576 | 5400 | 0.0001 | - |
|
279 |
+
| 0.1591 | 5450 | 0.0 | - |
|
280 |
+
| 0.1605 | 5500 | 0.0254 | - |
|
281 |
+
| 0.1620 | 5550 | 0.0001 | - |
|
282 |
+
| 0.1634 | 5600 | 0.0002 | - |
|
283 |
+
| 0.1649 | 5650 | 0.0 | - |
|
284 |
+
| 0.1664 | 5700 | 0.0264 | - |
|
285 |
+
| 0.1678 | 5750 | 0.0006 | - |
|
286 |
+
| 0.1693 | 5800 | 0.0001 | - |
|
287 |
+
| 0.1707 | 5850 | 0.0022 | - |
|
288 |
+
| 0.1722 | 5900 | 0.0011 | - |
|
289 |
+
| 0.1737 | 5950 | 0.1395 | - |
|
290 |
+
| 0.1751 | 6000 | 0.0169 | - |
|
291 |
+
| 0.1766 | 6050 | 0.0043 | - |
|
292 |
+
| 0.1780 | 6100 | 0.1513 | - |
|
293 |
+
| 0.1795 | 6150 | 0.0001 | - |
|
294 |
+
| 0.1809 | 6200 | 0.0008 | - |
|
295 |
+
| 0.1824 | 6250 | 0.0 | - |
|
296 |
+
| 0.1839 | 6300 | 0.0009 | - |
|
297 |
+
| 0.1853 | 6350 | 0.0002 | - |
|
298 |
+
| 0.1868 | 6400 | 0.0001 | - |
|
299 |
+
| 0.1882 | 6450 | 0.0002 | - |
|
300 |
+
| 0.1897 | 6500 | 0.0534 | - |
|
301 |
+
| 0.1912 | 6550 | 0.0002 | - |
|
302 |
+
| 0.1926 | 6600 | 0.0001 | - |
|
303 |
+
| 0.1941 | 6650 | 0.0007 | - |
|
304 |
+
| 0.1955 | 6700 | 0.1641 | - |
|
305 |
+
| 0.1970 | 6750 | 0.0001 | - |
|
306 |
+
| 0.1985 | 6800 | 0.0012 | - |
|
307 |
+
| 0.1999 | 6850 | 0.0035 | - |
|
308 |
+
| 0.2014 | 6900 | 0.0006 | - |
|
309 |
+
| 0.2028 | 6950 | 0.0001 | - |
|
310 |
+
| 0.2043 | 7000 | 0.0107 | - |
|
311 |
+
| 0.2058 | 7050 | 0.0001 | - |
|
312 |
+
| 0.2072 | 7100 | 0.0028 | - |
|
313 |
+
| 0.2087 | 7150 | 0.0004 | - |
|
314 |
+
| 0.2101 | 7200 | 0.0 | - |
|
315 |
+
| 0.2116 | 7250 | 0.0866 | - |
|
316 |
+
| 0.2131 | 7300 | 0.0 | - |
|
317 |
+
| 0.2145 | 7350 | 0.0001 | - |
|
318 |
+
| 0.2160 | 7400 | 0.0 | - |
|
319 |
+
| 0.2174 | 7450 | 0.0 | - |
|
320 |
+
| 0.2189 | 7500 | 0.0001 | - |
|
321 |
+
| 0.2203 | 7550 | 0.0 | - |
|
322 |
+
| 0.2218 | 7600 | 0.0001 | - |
|
323 |
+
| 0.2233 | 7650 | 0.0001 | - |
|
324 |
+
| 0.2247 | 7700 | 0.0 | - |
|
325 |
+
| 0.2262 | 7750 | 0.0532 | - |
|
326 |
+
| 0.2276 | 7800 | 0.0 | - |
|
327 |
+
| 0.2291 | 7850 | 0.0611 | - |
|
328 |
+
| 0.2306 | 7900 | 0.0001 | - |
|
329 |
+
| 0.2320 | 7950 | 0.0 | - |
|
330 |
+
| 0.2335 | 8000 | 0.0001 | - |
|
331 |
+
| 0.2349 | 8050 | 0.0 | - |
|
332 |
+
| 0.2364 | 8100 | 0.0 | - |
|
333 |
+
| 0.2379 | 8150 | 0.0 | - |
|
334 |
+
| 0.2393 | 8200 | 0.0304 | - |
|
335 |
+
| 0.2408 | 8250 | 0.0 | - |
|
336 |
+
| 0.2422 | 8300 | 0.0253 | - |
|
337 |
+
| 0.2437 | 8350 | 0.0 | - |
|
338 |
+
| 0.2452 | 8400 | 0.0 | - |
|
339 |
+
| 0.2466 | 8450 | 0.0 | - |
|
340 |
+
| 0.2481 | 8500 | 0.0173 | - |
|
341 |
+
| 0.2495 | 8550 | 0.0002 | - |
|
342 |
+
| 0.2510 | 8600 | 0.0003 | - |
|
343 |
+
| 0.2525 | 8650 | 0.0012 | - |
|
344 |
+
| 0.2539 | 8700 | 0.1639 | - |
|
345 |
+
| 0.2554 | 8750 | 0.0308 | - |
|
346 |
+
| 0.2568 | 8800 | 0.0 | - |
|
347 |
+
| 0.2583 | 8850 | 0.0 | - |
|
348 |
+
| 0.2597 | 8900 | 0.068 | - |
|
349 |
+
| 0.2612 | 8950 | 0.0001 | - |
|
350 |
+
| 0.2627 | 9000 | 0.0001 | - |
|
351 |
+
| 0.2641 | 9050 | 0.0 | - |
|
352 |
+
| 0.2656 | 9100 | 0.0734 | - |
|
353 |
+
| 0.2670 | 9150 | 0.0002 | - |
|
354 |
+
| 0.2685 | 9200 | 0.0 | - |
|
355 |
+
| 0.2700 | 9250 | 0.0244 | - |
|
356 |
+
| 0.2714 | 9300 | 0.1642 | - |
|
357 |
+
| 0.2729 | 9350 | 0.326 | - |
|
358 |
+
| 0.2743 | 9400 | 0.0023 | - |
|
359 |
+
| 0.2758 | 9450 | 0.1533 | - |
|
360 |
+
| 0.2773 | 9500 | 0.0003 | - |
|
361 |
+
| 0.2787 | 9550 | 0.0005 | - |
|
362 |
+
| 0.2802 | 9600 | 0.0005 | - |
|
363 |
+
| 0.2816 | 9650 | 0.0003 | - |
|
364 |
+
| 0.2831 | 9700 | 0.0001 | - |
|
365 |
+
| 0.2846 | 9750 | 0.0001 | - |
|
366 |
+
| 0.2860 | 9800 | 0.0003 | - |
|
367 |
+
| 0.2875 | 9850 | 0.0008 | - |
|
368 |
+
| 0.2889 | 9900 | 0.1625 | - |
|
369 |
+
| 0.2904 | 9950 | 0.0011 | - |
|
370 |
+
| 0.2919 | 10000 | 0.037 | - |
|
371 |
+
| 0.2933 | 10050 | 0.0006 | - |
|
372 |
+
| 0.2948 | 10100 | 0.0006 | - |
|
373 |
+
| 0.2962 | 10150 | 0.0001 | - |
|
374 |
+
| 0.2977 | 10200 | 0.0002 | - |
|
375 |
+
| 0.2991 | 10250 | 0.0149 | - |
|
376 |
+
| 0.3006 | 10300 | 0.0 | - |
|
377 |
+
| 0.3021 | 10350 | 0.0 | - |
|
378 |
+
| 0.3035 | 10400 | 0.0 | - |
|
379 |
+
| 0.3050 | 10450 | 0.0 | - |
|
380 |
+
| 0.3064 | 10500 | 0.0 | - |
|
381 |
+
| 0.3079 | 10550 | 0.0 | - |
|
382 |
+
| 0.3094 | 10600 | 0.0 | - |
|
383 |
+
| 0.3108 | 10650 | 0.0001 | - |
|
384 |
+
| 0.3123 | 10700 | 0.0932 | - |
|
385 |
+
| 0.3137 | 10750 | 0.0 | - |
|
386 |
+
| 0.3152 | 10800 | 0.0 | - |
|
387 |
+
| 0.3167 | 10850 | 0.0 | - |
|
388 |
+
| 0.3181 | 10900 | 0.0 | - |
|
389 |
+
| 0.3196 | 10950 | 0.0 | - |
|
390 |
+
| 0.3210 | 11000 | 0.0004 | - |
|
391 |
+
| 0.3225 | 11050 | 0.0 | - |
|
392 |
+
| 0.3240 | 11100 | 0.0 | - |
|
393 |
+
| 0.3254 | 11150 | 0.0 | - |
|
394 |
+
| 0.3269 | 11200 | 0.0228 | - |
|
395 |
+
| 0.3283 | 11250 | 0.0 | - |
|
396 |
+
| 0.3298 | 11300 | 0.0263 | - |
|
397 |
+
| 0.3313 | 11350 | 0.0001 | - |
|
398 |
+
| 0.3327 | 11400 | 0.0218 | - |
|
399 |
+
| 0.3342 | 11450 | 0.0 | - |
|
400 |
+
| 0.3356 | 11500 | 0.0826 | - |
|
401 |
+
| 0.3371 | 11550 | 0.0 | - |
|
402 |
+
| 0.3385 | 11600 | 0.0 | - |
|
403 |
+
| 0.3400 | 11650 | 0.0 | - |
|
404 |
+
| 0.3415 | 11700 | 0.0 | - |
|
405 |
+
| 0.3429 | 11750 | 0.0002 | - |
|
406 |
+
| 0.3444 | 11800 | 0.0 | - |
|
407 |
+
| 0.3458 | 11850 | 0.0001 | - |
|
408 |
+
| 0.3473 | 11900 | 0.0 | - |
|
409 |
+
| 0.3488 | 11950 | 0.0 | - |
|
410 |
+
| 0.3502 | 12000 | 0.0 | - |
|
411 |
+
| 0.3517 | 12050 | 0.0 | - |
|
412 |
+
| 0.3531 | 12100 | 0.0563 | - |
|
413 |
+
| 0.3546 | 12150 | 0.0 | - |
|
414 |
+
| 0.3561 | 12200 | 0.0384 | - |
|
415 |
+
| 0.3575 | 12250 | 0.0002 | - |
|
416 |
+
| 0.3590 | 12300 | 0.0352 | - |
|
417 |
+
| 0.3604 | 12350 | 0.0003 | - |
|
418 |
+
| 0.3619 | 12400 | 0.0001 | - |
|
419 |
+
| 0.3634 | 12450 | 0.0003 | - |
|
420 |
+
| 0.3648 | 12500 | 0.0 | - |
|
421 |
+
| 0.3663 | 12550 | 0.0003 | - |
|
422 |
+
| 0.3677 | 12600 | 0.0 | - |
|
423 |
+
| 0.3692 | 12650 | 0.0 | - |
|
424 |
+
| 0.3707 | 12700 | 0.0002 | - |
|
425 |
+
| 0.3721 | 12750 | 0.0002 | - |
|
426 |
+
| 0.3736 | 12800 | 0.0 | - |
|
427 |
+
| 0.3750 | 12850 | 0.0 | - |
|
428 |
+
| 0.3765 | 12900 | 0.0 | - |
|
429 |
+
| 0.3779 | 12950 | 0.0 | - |
|
430 |
+
| 0.3794 | 13000 | 0.0 | - |
|
431 |
+
| 0.3809 | 13050 | 0.0141 | - |
|
432 |
+
| 0.3823 | 13100 | 0.0 | - |
|
433 |
+
| 0.3838 | 13150 | 0.1085 | - |
|
434 |
+
| 0.3852 | 13200 | 0.0 | - |
|
435 |
+
| 0.3867 | 13250 | 0.0006 | - |
|
436 |
+
| 0.3882 | 13300 | 0.0778 | - |
|
437 |
+
| 0.3896 | 13350 | 0.0003 | - |
|
438 |
+
| 0.3911 | 13400 | 0.0001 | - |
|
439 |
+
| 0.3925 | 13450 | 0.0 | - |
|
440 |
+
| 0.3940 | 13500 | 0.0001 | - |
|
441 |
+
| 0.3955 | 13550 | 0.0 | - |
|
442 |
+
| 0.3969 | 13600 | 0.0001 | - |
|
443 |
+
| 0.3984 | 13650 | 0.0 | - |
|
444 |
+
| 0.3998 | 13700 | 0.0086 | - |
|
445 |
+
| 0.4013 | 13750 | 0.0079 | - |
|
446 |
+
| 0.4028 | 13800 | 0.0001 | - |
|
447 |
+
| 0.4042 | 13850 | 0.0001 | - |
|
448 |
+
| 0.4057 | 13900 | 0.084 | - |
|
449 |
+
| 0.4071 | 13950 | 0.0003 | - |
|
450 |
+
| 0.4086 | 14000 | 0.0004 | - |
|
451 |
+
| 0.4101 | 14050 | 0.0053 | - |
|
452 |
+
| 0.4115 | 14100 | 0.0 | - |
|
453 |
+
| 0.4130 | 14150 | 0.0008 | - |
|
454 |
+
| 0.4144 | 14200 | 0.1477 | - |
|
455 |
+
| 0.4159 | 14250 | 0.0 | - |
|
456 |
+
| 0.4173 | 14300 | 0.0017 | - |
|
457 |
+
| 0.4188 | 14350 | 0.0 | - |
|
458 |
+
| 0.4203 | 14400 | 0.0001 | - |
|
459 |
+
| 0.4217 | 14450 | 0.0414 | - |
|
460 |
+
| 0.4232 | 14500 | 0.0 | - |
|
461 |
+
| 0.4246 | 14550 | 0.0002 | - |
|
462 |
+
| 0.4261 | 14600 | 0.0627 | - |
|
463 |
+
| 0.4276 | 14650 | 0.1112 | - |
|
464 |
+
| 0.4290 | 14700 | 0.0 | - |
|
465 |
+
| 0.4305 | 14750 | 0.0002 | - |
|
466 |
+
| 0.4319 | 14800 | 0.0002 | - |
|
467 |
+
| 0.4334 | 14850 | 0.0393 | - |
|
468 |
+
| 0.4349 | 14900 | 0.0 | - |
|
469 |
+
| 0.4363 | 14950 | 0.0 | - |
|
470 |
+
| 0.4378 | 15000 | 0.0003 | - |
|
471 |
+
| 0.4392 | 15050 | 0.0001 | - |
|
472 |
+
| 0.4407 | 15100 | 0.0005 | - |
|
473 |
+
| 0.4422 | 15150 | 0.0009 | - |
|
474 |
+
| 0.4436 | 15200 | 0.0001 | - |
|
475 |
+
| 0.4451 | 15250 | 0.0001 | - |
|
476 |
+
| 0.4465 | 15300 | 0.0212 | - |
|
477 |
+
| 0.4480 | 15350 | 0.0 | - |
|
478 |
+
| 0.4495 | 15400 | 0.0 | - |
|
479 |
+
| 0.4509 | 15450 | 0.0 | - |
|
480 |
+
| 0.4524 | 15500 | 0.0 | - |
|
481 |
+
| 0.4538 | 15550 | 0.05 | - |
|
482 |
+
| 0.4553 | 15600 | 0.0 | - |
|
483 |
+
| 0.4567 | 15650 | 0.028 | - |
|
484 |
+
| 0.4582 | 15700 | 0.0001 | - |
|
485 |
+
| 0.4597 | 15750 | 0.0 | - |
|
486 |
+
| 0.4611 | 15800 | 0.0 | - |
|
487 |
+
| 0.4626 | 15850 | 0.0 | - |
|
488 |
+
| 0.4640 | 15900 | 0.0 | - |
|
489 |
+
| 0.4655 | 15950 | 0.043 | - |
|
490 |
+
| 0.4670 | 16000 | 0.0363 | - |
|
491 |
+
| 0.4684 | 16050 | 0.0 | - |
|
492 |
+
| 0.4699 | 16100 | 0.054 | - |
|
493 |
+
| 0.4713 | 16150 | 0.0 | - |
|
494 |
+
| 0.4728 | 16200 | 0.0 | - |
|
495 |
+
| 0.4743 | 16250 | 0.0 | - |
|
496 |
+
| 0.4757 | 16300 | 0.0 | - |
|
497 |
+
| 0.4772 | 16350 | 0.1 | - |
|
498 |
+
| 0.4786 | 16400 | 0.0001 | - |
|
499 |
+
| 0.4801 | 16450 | 0.0001 | - |
|
500 |
+
| 0.4816 | 16500 | 0.0 | - |
|
501 |
+
| 0.4830 | 16550 | 0.0267 | - |
|
502 |
+
| 0.4845 | 16600 | 0.0361 | - |
|
503 |
+
| 0.4859 | 16650 | 0.0 | - |
|
504 |
+
| 0.4874 | 16700 | 0.0181 | - |
|
505 |
+
| 0.4889 | 16750 | 0.0 | - |
|
506 |
+
| 0.4903 | 16800 | 0.0382 | - |
|
507 |
+
| 0.4918 | 16850 | 0.0276 | - |
|
508 |
+
| 0.4932 | 16900 | 0.0 | - |
|
509 |
+
| 0.4947 | 16950 | 0.0345 | - |
|
510 |
+
| 0.4961 | 17000 | 0.0 | - |
|
511 |
+
| 0.4976 | 17050 | 0.0 | - |
|
512 |
+
| 0.4991 | 17100 | 0.0 | - |
|
513 |
+
| 0.5005 | 17150 | 0.0 | - |
|
514 |
+
| 0.5020 | 17200 | 0.0 | - |
|
515 |
+
| 0.5034 | 17250 | 0.0 | - |
|
516 |
+
| 0.5049 | 17300 | 0.0001 | - |
|
517 |
+
| 0.5064 | 17350 | 0.0 | - |
|
518 |
+
| 0.5078 | 17400 | 0.0 | - |
|
519 |
+
| 0.5093 | 17450 | 0.0004 | - |
|
520 |
+
| 0.5107 | 17500 | 0.071 | - |
|
521 |
+
| 0.5122 | 17550 | 0.0 | - |
|
522 |
+
| 0.5137 | 17600 | 0.0 | - |
|
523 |
+
| 0.5151 | 17650 | 0.0 | - |
|
524 |
+
| 0.5166 | 17700 | 0.0239 | - |
|
525 |
+
| 0.5180 | 17750 | 0.0 | - |
|
526 |
+
| 0.5195 | 17800 | 0.0 | - |
|
527 |
+
| 0.5210 | 17850 | 0.0 | - |
|
528 |
+
| 0.5224 | 17900 | 0.0 | - |
|
529 |
+
| 0.5239 | 17950 | 0.0 | - |
|
530 |
+
| 0.5253 | 18000 | 0.0 | - |
|
531 |
+
| 0.5268 | 18050 | 0.0 | - |
|
532 |
+
| 0.5283 | 18100 | 0.0 | - |
|
533 |
+
| 0.5297 | 18150 | 0.0 | - |
|
534 |
+
| 0.5312 | 18200 | 0.0001 | - |
|
535 |
+
| 0.5326 | 18250 | 0.0 | - |
|
536 |
+
| 0.5341 | 18300 | 0.0 | - |
|
537 |
+
| 0.5355 | 18350 | 0.064 | - |
|
538 |
+
| 0.5370 | 18400 | 0.0 | - |
|
539 |
+
| 0.5385 | 18450 | 0.0 | - |
|
540 |
+
| 0.5399 | 18500 | 0.0 | - |
|
541 |
+
| 0.5414 | 18550 | 0.0499 | - |
|
542 |
+
| 0.5428 | 18600 | 0.0001 | - |
|
543 |
+
| 0.5443 | 18650 | 0.0 | - |
|
544 |
+
| 0.5458 | 18700 | 0.0 | - |
|
545 |
+
| 0.5472 | 18750 | 0.0002 | - |
|
546 |
+
| 0.5487 | 18800 | 0.0964 | - |
|
547 |
+
| 0.5501 | 18850 | 0.0 | - |
|
548 |
+
| 0.5516 | 18900 | 0.0 | - |
|
549 |
+
| 0.5531 | 18950 | 0.0 | - |
|
550 |
+
| 0.5545 | 19000 | 0.0 | - |
|
551 |
+
| 0.5560 | 19050 | 0.0001 | - |
|
552 |
+
| 0.5574 | 19100 | 0.0 | - |
|
553 |
+
| 0.5589 | 19150 | 0.0 | - |
|
554 |
+
| 0.5604 | 19200 | 0.0556 | - |
|
555 |
+
| 0.5618 | 19250 | 0.0715 | - |
|
556 |
+
| 0.5633 | 19300 | 0.0004 | - |
|
557 |
+
| 0.5647 | 19350 | 0.0 | - |
|
558 |
+
| 0.5662 | 19400 | 0.0 | - |
|
559 |
+
| 0.5677 | 19450 | 0.0 | - |
|
560 |
+
| 0.5691 | 19500 | 0.0001 | - |
|
561 |
+
| 0.5706 | 19550 | 0.0 | - |
|
562 |
+
| 0.5720 | 19600 | 0.0446 | - |
|
563 |
+
| 0.5735 | 19650 | 0.0 | - |
|
564 |
+
| 0.5749 | 19700 | 0.0 | - |
|
565 |
+
| 0.5764 | 19750 | 0.0 | - |
|
566 |
+
| 0.5779 | 19800 | 0.0324 | - |
|
567 |
+
| 0.5793 | 19850 | 0.0001 | - |
|
568 |
+
| 0.5808 | 19900 | 0.0001 | - |
|
569 |
+
| 0.5822 | 19950 | 0.0 | - |
|
570 |
+
| 0.5837 | 20000 | 0.0 | - |
|
571 |
+
| 0.5852 | 20050 | 0.0 | - |
|
572 |
+
| 0.5866 | 20100 | 0.0429 | - |
|
573 |
+
| 0.5881 | 20150 | 0.0 | - |
|
574 |
+
| 0.5895 | 20200 | 0.0 | - |
|
575 |
+
| 0.5910 | 20250 | 0.0 | - |
|
576 |
+
| 0.5925 | 20300 | 0.0 | - |
|
577 |
+
| 0.5939 | 20350 | 0.0 | - |
|
578 |
+
| 0.5954 | 20400 | 0.0 | - |
|
579 |
+
| 0.5968 | 20450 | 0.0214 | - |
|
580 |
+
| 0.5983 | 20500 | 0.0 | - |
|
581 |
+
| 0.5998 | 20550 | 0.0 | - |
|
582 |
+
| 0.6012 | 20600 | 0.0 | - |
|
583 |
+
| 0.6027 | 20650 | 0.0 | - |
|
584 |
+
| 0.6041 | 20700 | 0.0 | - |
|
585 |
+
| 0.6056 | 20750 | 0.0 | - |
|
586 |
+
| 0.6071 | 20800 | 0.0 | - |
|
587 |
+
| 0.6085 | 20850 | 0.0 | - |
|
588 |
+
| 0.6100 | 20900 | 0.0 | - |
|
589 |
+
| 0.6114 | 20950 | 0.0001 | - |
|
590 |
+
| 0.6129 | 21000 | 0.0 | - |
|
591 |
+
| 0.6143 | 21050 | 0.0 | - |
|
592 |
+
| 0.6158 | 21100 | 0.0 | - |
|
593 |
+
| 0.6173 | 21150 | 0.0 | - |
|
594 |
+
| 0.6187 | 21200 | 0.0 | - |
|
595 |
+
| 0.6202 | 21250 | 0.0 | - |
|
596 |
+
| 0.6216 | 21300 | 0.0402 | - |
|
597 |
+
| 0.6231 | 21350 | 0.0603 | - |
|
598 |
+
| 0.6246 | 21400 | 0.0 | - |
|
599 |
+
| 0.6260 | 21450 | 0.0 | - |
|
600 |
+
| 0.6275 | 21500 | 0.0 | - |
|
601 |
+
| 0.6289 | 21550 | 0.0 | - |
|
602 |
+
| 0.6304 | 21600 | 0.0 | - |
|
603 |
+
| 0.6319 | 21650 | 0.0238 | - |
|
604 |
+
| 0.6333 | 21700 | 0.0187 | - |
|
605 |
+
| 0.6348 | 21750 | 0.0 | - |
|
606 |
+
| 0.6362 | 21800 | 0.0 | - |
|
607 |
+
| 0.6377 | 21850 | 0.0 | - |
|
608 |
+
| 0.6392 | 21900 | 0.0325 | - |
|
609 |
+
| 0.6406 | 21950 | 0.0 | - |
|
610 |
+
| 0.6421 | 22000 | 0.0 | - |
|
611 |
+
| 0.6435 | 22050 | 0.0361 | - |
|
612 |
+
| 0.6450 | 22100 | 0.0 | - |
|
613 |
+
| 0.6465 | 22150 | 0.0853 | - |
|
614 |
+
| 0.6479 | 22200 | 0.0 | - |
|
615 |
+
| 0.6494 | 22250 | 0.0 | - |
|
616 |
+
| 0.6508 | 22300 | 0.0 | - |
|
617 |
+
| 0.6523 | 22350 | 0.0 | - |
|
618 |
+
| 0.6537 | 22400 | 0.0649 | - |
|
619 |
+
| 0.6552 | 22450 | 0.0 | - |
|
620 |
+
| 0.6567 | 22500 | 0.0 | - |
|
621 |
+
| 0.6581 | 22550 | 0.0 | - |
|
622 |
+
| 0.6596 | 22600 | 0.0 | - |
|
623 |
+
| 0.6610 | 22650 | 0.0 | - |
|
624 |
+
| 0.6625 | 22700 | 0.0 | - |
|
625 |
+
| 0.6640 | 22750 | 0.0 | - |
|
626 |
+
| 0.6654 | 22800 | 0.0 | - |
|
627 |
+
| 0.6669 | 22850 | 0.0382 | - |
|
628 |
+
| 0.6683 | 22900 | 0.0 | - |
|
629 |
+
| 0.6698 | 22950 | 0.0 | - |
|
630 |
+
| 0.6713 | 23000 | 0.0 | - |
|
631 |
+
| 0.6727 | 23050 | 0.0 | - |
|
632 |
+
| 0.6742 | 23100 | 0.0 | - |
|
633 |
+
| 0.6756 | 23150 | 0.0 | - |
|
634 |
+
| 0.6771 | 23200 | 0.0001 | - |
|
635 |
+
| 0.6786 | 23250 | 0.0458 | - |
|
636 |
+
| 0.6800 | 23300 | 0.0 | - |
|
637 |
+
| 0.6815 | 23350 | 0.0 | - |
|
638 |
+
| 0.6829 | 23400 | 0.0 | - |
|
639 |
+
| 0.6844 | 23450 | 0.0 | - |
|
640 |
+
| 0.6859 | 23500 | 0.0 | - |
|
641 |
+
| 0.6873 | 23550 | 0.0 | - |
|
642 |
+
| 0.6888 | 23600 | 0.044 | - |
|
643 |
+
| 0.6902 | 23650 | 0.0 | - |
|
644 |
+
| 0.6917 | 23700 | 0.0406 | - |
|
645 |
+
| 0.6931 | 23750 | 0.0 | - |
|
646 |
+
| 0.6946 | 23800 | 0.0318 | - |
|
647 |
+
| 0.6961 | 23850 | 0.0306 | - |
|
648 |
+
| 0.6975 | 23900 | 0.077 | - |
|
649 |
+
| 0.6990 | 23950 | 0.0194 | - |
|
650 |
+
| 0.7004 | 24000 | 0.0 | - |
|
651 |
+
| 0.7019 | 24050 | 0.0 | - |
|
652 |
+
| 0.7034 | 24100 | 0.0 | - |
|
653 |
+
| 0.7048 | 24150 | 0.0 | - |
|
654 |
+
| 0.7063 | 24200 | 0.0 | - |
|
655 |
+
| 0.7077 | 24250 | 0.0 | - |
|
656 |
+
| 0.7092 | 24300 | 0.0 | - |
|
657 |
+
| 0.7107 | 24350 | 0.0521 | - |
|
658 |
+
| 0.7121 | 24400 | 0.0 | - |
|
659 |
+
| 0.7136 | 24450 | 0.0 | - |
|
660 |
+
| 0.7150 | 24500 | 0.0 | - |
|
661 |
+
| 0.7165 | 24550 | 0.0 | - |
|
662 |
+
| 0.7180 | 24600 | 0.0 | - |
|
663 |
+
| 0.7194 | 24650 | 0.0 | - |
|
664 |
+
| 0.7209 | 24700 | 0.0 | - |
|
665 |
+
| 0.7223 | 24750 | 0.0518 | - |
|
666 |
+
| 0.7238 | 24800 | 0.0 | - |
|
667 |
+
| 0.7253 | 24850 | 0.0 | - |
|
668 |
+
| 0.7267 | 24900 | 0.0475 | - |
|
669 |
+
| 0.7282 | 24950 | 0.0 | - |
|
670 |
+
| 0.7296 | 25000 | 0.0 | - |
|
671 |
+
| 0.7311 | 25050 | 0.0374 | - |
|
672 |
+
| 0.7325 | 25100 | 0.0 | - |
|
673 |
+
| 0.7340 | 25150 | 0.0 | - |
|
674 |
+
| 0.7355 | 25200 | 0.0345 | - |
|
675 |
+
| 0.7369 | 25250 | 0.0 | - |
|
676 |
+
| 0.7384 | 25300 | 0.0 | - |
|
677 |
+
| 0.7398 | 25350 | 0.1585 | - |
|
678 |
+
| 0.7413 | 25400 | 0.0007 | - |
|
679 |
+
| 0.7428 | 25450 | 0.1661 | - |
|
680 |
+
| 0.7442 | 25500 | 0.0 | - |
|
681 |
+
| 0.7457 | 25550 | 0.0 | - |
|
682 |
+
| 0.7471 | 25600 | 0.0 | - |
|
683 |
+
| 0.7486 | 25650 | 0.0 | - |
|
684 |
+
| 0.7501 | 25700 | 0.0001 | - |
|
685 |
+
| 0.7515 | 25750 | 0.0 | - |
|
686 |
+
| 0.7530 | 25800 | 0.0 | - |
|
687 |
+
| 0.7544 | 25850 | 0.1657 | - |
|
688 |
+
| 0.7559 | 25900 | 0.0 | - |
|
689 |
+
| 0.7574 | 25950 | 0.0002 | - |
|
690 |
+
| 0.7588 | 26000 | 0.0001 | - |
|
691 |
+
| 0.7603 | 26050 | 0.0004 | - |
|
692 |
+
| 0.7617 | 26100 | 0.0 | - |
|
693 |
+
| 0.7632 | 26150 | 0.0449 | - |
|
694 |
+
| 0.7647 | 26200 | 0.1664 | - |
|
695 |
+
| 0.7661 | 26250 | 0.0002 | - |
|
696 |
+
| 0.7676 | 26300 | 0.0 | - |
|
697 |
+
| 0.7690 | 26350 | 0.0 | - |
|
698 |
+
| 0.7705 | 26400 | 0.0 | - |
|
699 |
+
| 0.7719 | 26450 | 0.0 | - |
|
700 |
+
| 0.7734 | 26500 | 0.0464 | - |
|
701 |
+
| 0.7749 | 26550 | 0.0 | - |
|
702 |
+
| 0.7763 | 26600 | 0.0002 | - |
|
703 |
+
| 0.7778 | 26650 | 0.0 | - |
|
704 |
+
| 0.7792 | 26700 | 0.0 | - |
|
705 |
+
| 0.7807 | 26750 | 0.0001 | - |
|
706 |
+
| 0.7822 | 26800 | 0.038 | - |
|
707 |
+
| 0.7836 | 26850 | 0.0 | - |
|
708 |
+
| 0.7851 | 26900 | 0.0 | - |
|
709 |
+
| 0.7865 | 26950 | 0.0 | - |
|
710 |
+
| 0.7880 | 27000 | 0.0 | - |
|
711 |
+
| 0.7895 | 27050 | 0.0 | - |
|
712 |
+
| 0.7909 | 27100 | 0.0 | - |
|
713 |
+
| 0.7924 | 27150 | 0.0464 | - |
|
714 |
+
| 0.7938 | 27200 | 0.0001 | - |
|
715 |
+
| 0.7953 | 27250 | 0.0376 | - |
|
716 |
+
| 0.7968 | 27300 | 0.0 | - |
|
717 |
+
| 0.7982 | 27350 | 0.0 | - |
|
718 |
+
| 0.7997 | 27400 | 0.0001 | - |
|
719 |
+
| 0.8011 | 27450 | 0.0001 | - |
|
720 |
+
| 0.8026 | 27500 | 0.0431 | - |
|
721 |
+
| 0.8041 | 27550 | 0.0 | - |
|
722 |
+
| 0.8055 | 27600 | 0.0263 | - |
|
723 |
+
| 0.8070 | 27650 | 0.0 | - |
|
724 |
+
| 0.8084 | 27700 | 0.0 | - |
|
725 |
+
| 0.8099 | 27750 | 0.0001 | - |
|
726 |
+
| 0.8113 | 27800 | 0.0 | - |
|
727 |
+
| 0.8128 | 27850 | 0.0 | - |
|
728 |
+
| 0.8143 | 27900 | 0.0 | - |
|
729 |
+
| 0.8157 | 27950 | 0.0 | - |
|
730 |
+
| 0.8172 | 28000 | 0.0 | - |
|
731 |
+
| 0.8186 | 28050 | 0.0 | - |
|
732 |
+
| 0.8201 | 28100 | 0.0 | - |
|
733 |
+
| 0.8216 | 28150 | 0.0 | - |
|
734 |
+
| 0.8230 | 28200 | 0.0 | - |
|
735 |
+
| 0.8245 | 28250 | 0.0 | - |
|
736 |
+
| 0.8259 | 28300 | 0.0 | - |
|
737 |
+
| 0.8274 | 28350 | 0.0 | - |
|
738 |
+
| 0.8289 | 28400 | 0.0 | - |
|
739 |
+
| 0.8303 | 28450 | 0.0253 | - |
|
740 |
+
| 0.8318 | 28500 | 0.0603 | - |
|
741 |
+
| 0.8332 | 28550 | 0.0 | - |
|
742 |
+
| 0.8347 | 28600 | 0.0627 | - |
|
743 |
+
| 0.8362 | 28650 | 0.0 | - |
|
744 |
+
| 0.8376 | 28700 | 0.0659 | - |
|
745 |
+
| 0.8391 | 28750 | 0.0 | - |
|
746 |
+
| 0.8405 | 28800 | 0.0 | - |
|
747 |
+
| 0.8420 | 28850 | 0.0 | - |
|
748 |
+
| 0.8435 | 28900 | 0.0 | - |
|
749 |
+
| 0.8449 | 28950 | 0.0 | - |
|
750 |
+
| 0.8464 | 29000 | 0.0 | - |
|
751 |
+
| 0.8478 | 29050 | 0.0 | - |
|
752 |
+
| 0.8493 | 29100 | 0.0314 | - |
|
753 |
+
| 0.8507 | 29150 | 0.0002 | - |
|
754 |
+
| 0.8522 | 29200 | 0.0 | - |
|
755 |
+
| 0.8537 | 29250 | 0.0001 | - |
|
756 |
+
| 0.8551 | 29300 | 0.0 | - |
|
757 |
+
| 0.8566 | 29350 | 0.0 | - |
|
758 |
+
| 0.8580 | 29400 | 0.0 | - |
|
759 |
+
| 0.8595 | 29450 | 0.1661 | - |
|
760 |
+
| 0.8610 | 29500 | 0.0 | - |
|
761 |
+
| 0.8624 | 29550 | 0.0 | - |
|
762 |
+
| 0.8639 | 29600 | 0.0464 | - |
|
763 |
+
| 0.8653 | 29650 | 0.0 | - |
|
764 |
+
| 0.8668 | 29700 | 0.0 | - |
|
765 |
+
| 0.8683 | 29750 | 0.0 | - |
|
766 |
+
| 0.8697 | 29800 | 0.0387 | - |
|
767 |
+
| 0.8712 | 29850 | 0.0872 | - |
|
768 |
+
| 0.8726 | 29900 | 0.0638 | - |
|
769 |
+
| 0.8741 | 29950 | 0.0 | - |
|
770 |
+
| 0.8756 | 30000 | 0.0638 | - |
|
771 |
+
| 0.8770 | 30050 | 0.0 | - |
|
772 |
+
| 0.8785 | 30100 | 0.0431 | - |
|
773 |
+
| 0.8799 | 30150 | 0.0 | - |
|
774 |
+
| 0.8814 | 30200 | 0.0397 | - |
|
775 |
+
| 0.8829 | 30250 | 0.0379 | - |
|
776 |
+
| 0.8843 | 30300 | 0.0642 | - |
|
777 |
+
| 0.8858 | 30350 | 0.0 | - |
|
778 |
+
| 0.8872 | 30400 | 0.0652 | - |
|
779 |
+
| 0.8887 | 30450 | 0.0641 | - |
|
780 |
+
| 0.8901 | 30500 | 0.0 | - |
|
781 |
+
| 0.8916 | 30550 | 0.0 | - |
|
782 |
+
| 0.8931 | 30600 | 0.021 | - |
|
783 |
+
| 0.8945 | 30650 | 0.0 | - |
|
784 |
+
| 0.8960 | 30700 | 0.0218 | - |
|
785 |
+
| 0.8974 | 30750 | 0.0 | - |
|
786 |
+
| 0.8989 | 30800 | 0.0 | - |
|
787 |
+
| 0.9004 | 30850 | 0.0214 | - |
|
788 |
+
| 0.9018 | 30900 | 0.0 | - |
|
789 |
+
| 0.9033 | 30950 | 0.0 | - |
|
790 |
+
| 0.9047 | 31000 | 0.0 | - |
|
791 |
+
| 0.9062 | 31050 | 0.0717 | - |
|
792 |
+
| 0.9077 | 31100 | 0.0 | - |
|
793 |
+
| 0.9091 | 31150 | 0.0476 | - |
|
794 |
+
| 0.9106 | 31200 | 0.0 | - |
|
795 |
+
| 0.9120 | 31250 | 0.0 | - |
|
796 |
+
| 0.9135 | 31300 | 0.0 | - |
|
797 |
+
| 0.9150 | 31350 | 0.0 | - |
|
798 |
+
| 0.9164 | 31400 | 0.0 | - |
|
799 |
+
| 0.9179 | 31450 | 0.0 | - |
|
800 |
+
| 0.9193 | 31500 | 0.0548 | - |
|
801 |
+
| 0.9208 | 31550 | 0.0002 | - |
|
802 |
+
| 0.9223 | 31600 | 0.0 | - |
|
803 |
+
| 0.9237 | 31650 | 0.0 | - |
|
804 |
+
| 0.9252 | 31700 | 0.0 | - |
|
805 |
+
| 0.9266 | 31750 | 0.0 | - |
|
806 |
+
| 0.9281 | 31800 | 0.0 | - |
|
807 |
+
| 0.9295 | 31850 | 0.0 | - |
|
808 |
+
| 0.9310 | 31900 | 0.0 | - |
|
809 |
+
| 0.9325 | 31950 | 0.0 | - |
|
810 |
+
| 0.9339 | 32000 | 0.0358 | - |
|
811 |
+
| 0.9354 | 32050 | 0.0 | - |
|
812 |
+
| 0.9368 | 32100 | 0.0 | - |
|
813 |
+
| 0.9383 | 32150 | 0.0 | - |
|
814 |
+
| 0.9398 | 32200 | 0.0 | - |
|
815 |
+
| 0.9412 | 32250 | 0.0 | - |
|
816 |
+
| 0.9427 | 32300 | 0.0 | - |
|
817 |
+
| 0.9441 | 32350 | 0.0 | - |
|
818 |
+
| 0.9456 | 32400 | 0.0 | - |
|
819 |
+
| 0.9471 | 32450 | 0.0 | - |
|
820 |
+
| 0.9485 | 32500 | 0.0 | - |
|
821 |
+
| 0.9500 | 32550 | 0.0863 | - |
|
822 |
+
| 0.9514 | 32600 | 0.0 | - |
|
823 |
+
| 0.9529 | 32650 | 0.0 | - |
|
824 |
+
| 0.9544 | 32700 | 0.0 | - |
|
825 |
+
| 0.9558 | 32750 | 0.0 | - |
|
826 |
+
| 0.9573 | 32800 | 0.0 | - |
|
827 |
+
| 0.9587 | 32850 | 0.0 | - |
|
828 |
+
| 0.9602 | 32900 | 0.0 | - |
|
829 |
+
| 0.9617 | 32950 | 0.0 | - |
|
830 |
+
| 0.9631 | 33000 | 0.0241 | - |
|
831 |
+
| 0.9646 | 33050 | 0.0 | - |
|
832 |
+
| 0.9660 | 33100 | 0.0 | - |
|
833 |
+
| 0.9675 | 33150 | 0.0 | - |
|
834 |
+
| 0.9689 | 33200 | 0.0258 | - |
|
835 |
+
| 0.9704 | 33250 | 0.0 | - |
|
836 |
+
| 0.9719 | 33300 | 0.0 | - |
|
837 |
+
| 0.9733 | 33350 | 0.0 | - |
|
838 |
+
| 0.9748 | 33400 | 0.0 | - |
|
839 |
+
| 0.9762 | 33450 | 0.0 | - |
|
840 |
+
| 0.9777 | 33500 | 0.0 | - |
|
841 |
+
| 0.9792 | 33550 | 0.0 | - |
|
842 |
+
| 0.9806 | 33600 | 0.0 | - |
|
843 |
+
| 0.9821 | 33650 | 0.0 | - |
|
844 |
+
| 0.9835 | 33700 | 0.0605 | - |
|
845 |
+
| 0.9850 | 33750 | 0.0 | - |
|
846 |
+
| 0.9865 | 33800 | 0.0 | - |
|
847 |
+
| 0.9879 | 33850 | 0.0 | - |
|
848 |
+
| 0.9894 | 33900 | 0.0245 | - |
|
849 |
+
| 0.9908 | 33950 | 0.0 | - |
|
850 |
+
| 0.9923 | 34000 | 0.0 | - |
|
851 |
+
| 0.9938 | 34050 | 0.0585 | - |
|
852 |
+
| 0.9952 | 34100 | 0.0513 | - |
|
853 |
+
| 0.9967 | 34150 | 0.0 | - |
|
854 |
+
| 0.9981 | 34200 | 0.0 | - |
|
855 |
+
| 0.9996 | 34250 | 0.0 | - |
|
856 |
+
|
857 |
+
### Framework Versions
|
858 |
+
- Python: 3.10.13
|
859 |
+
- SetFit: 1.0.3
|
860 |
+
- Sentence Transformers: 2.7.0
|
861 |
+
- spaCy: 3.7.4
|
862 |
+
- Transformers: 4.36.2
|
863 |
+
- PyTorch: 2.1.2
|
864 |
+
- Datasets: 2.18.0
|
865 |
+
- Tokenizers: 0.15.2
|
866 |
+
|
867 |
+
## Citation
|
868 |
+
|
869 |
+
### BibTeX
|
870 |
+
```bibtex
|
871 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
872 |
+
doi = {10.48550/ARXIV.2209.11055},
|
873 |
+
url = {https://arxiv.org/abs/2209.11055},
|
874 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
875 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
876 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
877 |
+
publisher = {arXiv},
|
878 |
+
year = {2022},
|
879 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
880 |
+
}
|
881 |
+
```
|
882 |
+
|
883 |
+
<!--
|
884 |
+
## Glossary
|
885 |
+
|
886 |
+
*Clearly define terms in order to be accessible across audiences.*
|
887 |
+
-->
|
888 |
+
|
889 |
+
<!--
|
890 |
+
## Model Card Authors
|
891 |
+
|
892 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
893 |
+
-->
|
894 |
+
|
895 |
+
<!--
|
896 |
+
## Model Card Contact
|
897 |
+
|
898 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
899 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "firqaaa/indo-setfit-absa-bert-base-restaurants-aspect",
|
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 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.20.1",
|
5 |
+
"pytorch": "1.11.0"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null
|
9 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"labels": [
|
3 |
+
"no aspect",
|
4 |
+
"aspect"
|
5 |
+
],
|
6 |
+
"normalize_embeddings": false,
|
7 |
+
"spacy_model": "id_core_news_trf",
|
8 |
+
"span_context": 0
|
9 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:76f911ca8903c8bfa6093349b2936e93ac0c72d24308757acf2c6fadd8800771
|
3 |
+
size 497787752
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bb3dfd3a803d8397c371fcfc30abac29c1488cc6d179ff8cbf86c33d9afd2c22
|
3 |
+
size 6991
|
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 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
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|
5 |
+
"normalized": false,
|
6 |
+
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|
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
|
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+
}
|
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+
}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
@@ -0,0 +1,64 @@
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|
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
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