Commit
•
0f24b48
1
Parent(s):
c31b3a7
Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +9 -0
- README.md +418 -0
- config.json +31 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +7 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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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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}
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README.md
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|
| 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: 'fuel_network Fuel The worlds fastest modular execution layer Sway Language '
|
| 12 |
+
- text: 'enjin Enjin Enjin Blockchain allows seamless no code integration of NFTs
|
| 13 |
+
in video games and other platforms with NFT functions at the protocol level '
|
| 14 |
+
- text: 'bobbyclee Bobby Lee Ballet Worlds EASIEST Cold Storage Founder CEO of was
|
| 15 |
+
Board Member Cofounder BTCChina BTCC Author of The Promise of Bitcoin available
|
| 16 |
+
on '
|
| 17 |
+
- text: 'tradermayne Mayne '
|
| 18 |
+
- text: 'novogratz Mike Novogratz CEO GLXY CN Early Investormushroom TheBailProject
|
| 19 |
+
Disclaimer '
|
| 20 |
+
pipeline_tag: text-classification
|
| 21 |
+
inference: true
|
| 22 |
+
base_model: BAAI/bge-small-en-v1.5
|
| 23 |
+
model-index:
|
| 24 |
+
- name: SetFit with BAAI/bge-small-en-v1.5
|
| 25 |
+
results:
|
| 26 |
+
- task:
|
| 27 |
+
type: text-classification
|
| 28 |
+
name: Text Classification
|
| 29 |
+
dataset:
|
| 30 |
+
name: Unknown
|
| 31 |
+
type: unknown
|
| 32 |
+
split: test
|
| 33 |
+
metrics:
|
| 34 |
+
- type: accuracy
|
| 35 |
+
value: 0.99
|
| 36 |
+
name: Accuracy
|
| 37 |
+
---
|
| 38 |
+
|
| 39 |
+
# SetFit with BAAI/bge-small-en-v1.5
|
| 40 |
+
|
| 41 |
+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
|
| 42 |
+
|
| 43 |
+
The model has been trained using an efficient few-shot learning technique that involves:
|
| 44 |
+
|
| 45 |
+
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
|
| 46 |
+
2. Training a classification head with features from the fine-tuned Sentence Transformer.
|
| 47 |
+
|
| 48 |
+
## Model Details
|
| 49 |
+
|
| 50 |
+
### Model Description
|
| 51 |
+
- **Model Type:** SetFit
|
| 52 |
+
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
|
| 53 |
+
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
| 54 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 55 |
+
- **Number of Classes:** 2 classes
|
| 56 |
+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
| 57 |
+
<!-- - **Language:** Unknown -->
|
| 58 |
+
<!-- - **License:** Unknown -->
|
| 59 |
+
|
| 60 |
+
### Model Sources
|
| 61 |
+
|
| 62 |
+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
| 63 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
| 64 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 65 |
+
|
| 66 |
+
### Model Labels
|
| 67 |
+
| Label | Examples |
|
| 68 |
+
|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 69 |
+
| ORGANIZATIONAL | <ul><li>'cryptonewton Shelby BitGet partner '</li><li>'trezor Trezor Crypto security made easy'</li><li>'forbes Forbes Sign up now for Forbes free daily newsletter for unmatched insights and exclusive reporting '</li></ul> |
|
| 70 |
+
| INDIVIDUAL | <ul><li>'anbessa100 ANBESSA No paid service Never DM u'</li><li>'sbf_ftx SBF '</li><li>'machibigbrother Machi Big Brother '</li></ul> |
|
| 71 |
+
|
| 72 |
+
## Evaluation
|
| 73 |
+
|
| 74 |
+
### Metrics
|
| 75 |
+
| Label | Accuracy |
|
| 76 |
+
|:--------|:---------|
|
| 77 |
+
| **all** | 0.99 |
|
| 78 |
+
|
| 79 |
+
## Uses
|
| 80 |
+
|
| 81 |
+
### Direct Use for Inference
|
| 82 |
+
|
| 83 |
+
First install the SetFit library:
|
| 84 |
+
|
| 85 |
+
```bash
|
| 86 |
+
pip install setfit
|
| 87 |
+
```
|
| 88 |
+
|
| 89 |
+
Then you can load this model and run inference.
|
| 90 |
+
|
| 91 |
+
```python
|
| 92 |
+
from setfit import SetFitModel
|
| 93 |
+
|
| 94 |
+
# Download from the 🤗 Hub
|
| 95 |
+
model = SetFitModel.from_pretrained("kasparas12/is_organizational_model")
|
| 96 |
+
# Run inference
|
| 97 |
+
preds = model("tradermayne Mayne ")
|
| 98 |
+
```
|
| 99 |
+
|
| 100 |
+
<!--
|
| 101 |
+
### Downstream Use
|
| 102 |
+
|
| 103 |
+
*List how someone could finetune this model on their own dataset.*
|
| 104 |
+
-->
|
| 105 |
+
|
| 106 |
+
<!--
|
| 107 |
+
### Out-of-Scope Use
|
| 108 |
+
|
| 109 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 110 |
+
-->
|
| 111 |
+
|
| 112 |
+
<!--
|
| 113 |
+
## Bias, Risks and Limitations
|
| 114 |
+
|
| 115 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 116 |
+
-->
|
| 117 |
+
|
| 118 |
+
<!--
|
| 119 |
+
### Recommendations
|
| 120 |
+
|
| 121 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 122 |
+
-->
|
| 123 |
+
|
| 124 |
+
## Training Details
|
| 125 |
+
|
| 126 |
+
### Training Set Metrics
|
| 127 |
+
| Training set | Min | Median | Max |
|
| 128 |
+
|:-------------|:----|:--------|:----|
|
| 129 |
+
| Word count | 3 | 15.7338 | 35 |
|
| 130 |
+
|
| 131 |
+
| Label | Training Sample Count |
|
| 132 |
+
|:---------------|:----------------------|
|
| 133 |
+
| INDIVIDUAL | 423 |
|
| 134 |
+
| ORGANIZATIONAL | 377 |
|
| 135 |
+
|
| 136 |
+
### Training Hyperparameters
|
| 137 |
+
- batch_size: (32, 32)
|
| 138 |
+
- num_epochs: (1, 1)
|
| 139 |
+
- max_steps: -1
|
| 140 |
+
- sampling_strategy: oversampling
|
| 141 |
+
- body_learning_rate: (2e-05, 1e-05)
|
| 142 |
+
- head_learning_rate: 0.01
|
| 143 |
+
- loss: CosineSimilarityLoss
|
| 144 |
+
- distance_metric: cosine_distance
|
| 145 |
+
- margin: 0.25
|
| 146 |
+
- end_to_end: False
|
| 147 |
+
- use_amp: False
|
| 148 |
+
- warmup_proportion: 0.1
|
| 149 |
+
- seed: 42
|
| 150 |
+
- eval_max_steps: -1
|
| 151 |
+
- load_best_model_at_end: False
|
| 152 |
+
|
| 153 |
+
### Training Results
|
| 154 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 155 |
+
|:------:|:-----:|:-------------:|:---------------:|
|
| 156 |
+
| 0.0016 | 1 | 0.2511 | - |
|
| 157 |
+
| 0.0789 | 50 | 0.2505 | - |
|
| 158 |
+
| 0.1577 | 100 | 0.2225 | - |
|
| 159 |
+
| 0.2366 | 150 | 0.2103 | - |
|
| 160 |
+
| 0.3155 | 200 | 0.1383 | - |
|
| 161 |
+
| 0.3943 | 250 | 0.0329 | - |
|
| 162 |
+
| 0.4732 | 300 | 0.0098 | - |
|
| 163 |
+
| 0.5521 | 350 | 0.0034 | - |
|
| 164 |
+
| 0.6309 | 400 | 0.0019 | - |
|
| 165 |
+
| 0.7098 | 450 | 0.0015 | - |
|
| 166 |
+
| 0.7886 | 500 | 0.0014 | - |
|
| 167 |
+
| 0.8675 | 550 | 0.0012 | - |
|
| 168 |
+
| 0.0001 | 1 | 0.2524 | - |
|
| 169 |
+
| 0.0050 | 50 | 0.2115 | - |
|
| 170 |
+
| 0.0099 | 100 | 0.193 | - |
|
| 171 |
+
| 0.0001 | 1 | 0.2424 | - |
|
| 172 |
+
| 0.0050 | 50 | 0.2038 | - |
|
| 173 |
+
| 0.0099 | 100 | 0.1782 | - |
|
| 174 |
+
| 0.0001 | 1 | 0.2208 | - |
|
| 175 |
+
| 0.0050 | 50 | 0.1931 | - |
|
| 176 |
+
| 0.0099 | 100 | 0.1629 | - |
|
| 177 |
+
| 0.0149 | 150 | 0.2716 | - |
|
| 178 |
+
| 0.0199 | 200 | 0.18 | - |
|
| 179 |
+
| 0.0249 | 250 | 0.2504 | - |
|
| 180 |
+
| 0.0298 | 300 | 0.1936 | - |
|
| 181 |
+
| 0.0348 | 350 | 0.1764 | - |
|
| 182 |
+
| 0.0398 | 400 | 0.1817 | - |
|
| 183 |
+
| 0.0447 | 450 | 0.0624 | - |
|
| 184 |
+
| 0.0497 | 500 | 0.1183 | - |
|
| 185 |
+
| 0.0547 | 550 | 0.0793 | - |
|
| 186 |
+
| 0.0596 | 600 | 0.0281 | - |
|
| 187 |
+
| 0.0646 | 650 | 0.0876 | - |
|
| 188 |
+
| 0.0696 | 700 | 0.1701 | - |
|
| 189 |
+
| 0.0746 | 750 | 0.0468 | - |
|
| 190 |
+
| 0.0795 | 800 | 0.0525 | - |
|
| 191 |
+
| 0.0845 | 850 | 0.0783 | - |
|
| 192 |
+
| 0.0895 | 900 | 0.0342 | - |
|
| 193 |
+
| 0.0944 | 950 | 0.0158 | - |
|
| 194 |
+
| 0.0994 | 1000 | 0.0286 | - |
|
| 195 |
+
| 0.1044 | 1050 | 0.0016 | - |
|
| 196 |
+
| 0.1094 | 1100 | 0.0014 | - |
|
| 197 |
+
| 0.1143 | 1150 | 0.0298 | - |
|
| 198 |
+
| 0.1193 | 1200 | 0.018 | - |
|
| 199 |
+
| 0.1243 | 1250 | 0.0299 | - |
|
| 200 |
+
| 0.1292 | 1300 | 0.0019 | - |
|
| 201 |
+
| 0.1342 | 1350 | 0.0253 | - |
|
| 202 |
+
| 0.1392 | 1400 | 0.0009 | - |
|
| 203 |
+
| 0.1441 | 1450 | 0.0009 | - |
|
| 204 |
+
| 0.1491 | 1500 | 0.0011 | - |
|
| 205 |
+
| 0.1541 | 1550 | 0.0006 | - |
|
| 206 |
+
| 0.1591 | 1600 | 0.0006 | - |
|
| 207 |
+
| 0.1640 | 1650 | 0.0008 | - |
|
| 208 |
+
| 0.1690 | 1700 | 0.0005 | - |
|
| 209 |
+
| 0.1740 | 1750 | 0.0007 | - |
|
| 210 |
+
| 0.1789 | 1800 | 0.0006 | - |
|
| 211 |
+
| 0.1839 | 1850 | 0.0006 | - |
|
| 212 |
+
| 0.1889 | 1900 | 0.0006 | - |
|
| 213 |
+
| 0.1939 | 1950 | 0.0012 | - |
|
| 214 |
+
| 0.1988 | 2000 | 0.0004 | - |
|
| 215 |
+
| 0.2038 | 2050 | 0.0006 | - |
|
| 216 |
+
| 0.2088 | 2100 | 0.0005 | - |
|
| 217 |
+
| 0.2137 | 2150 | 0.0005 | - |
|
| 218 |
+
| 0.2187 | 2200 | 0.0005 | - |
|
| 219 |
+
| 0.2237 | 2250 | 0.0004 | - |
|
| 220 |
+
| 0.2287 | 2300 | 0.0005 | - |
|
| 221 |
+
| 0.2336 | 2350 | 0.0004 | - |
|
| 222 |
+
| 0.2386 | 2400 | 0.0004 | - |
|
| 223 |
+
| 0.2436 | 2450 | 0.0003 | - |
|
| 224 |
+
| 0.2485 | 2500 | 0.0004 | - |
|
| 225 |
+
| 0.2535 | 2550 | 0.0004 | - |
|
| 226 |
+
| 0.2585 | 2600 | 0.0004 | - |
|
| 227 |
+
| 0.2634 | 2650 | 0.0004 | - |
|
| 228 |
+
| 0.2684 | 2700 | 0.0004 | - |
|
| 229 |
+
| 0.2734 | 2750 | 0.0004 | - |
|
| 230 |
+
| 0.2784 | 2800 | 0.0056 | - |
|
| 231 |
+
| 0.2833 | 2850 | 0.0004 | - |
|
| 232 |
+
| 0.2883 | 2900 | 0.0003 | - |
|
| 233 |
+
| 0.2933 | 2950 | 0.0003 | - |
|
| 234 |
+
| 0.2982 | 3000 | 0.0004 | - |
|
| 235 |
+
| 0.3032 | 3050 | 0.0003 | - |
|
| 236 |
+
| 0.3082 | 3100 | 0.0003 | - |
|
| 237 |
+
| 0.3132 | 3150 | 0.0003 | - |
|
| 238 |
+
| 0.3181 | 3200 | 0.0003 | - |
|
| 239 |
+
| 0.3231 | 3250 | 0.0004 | - |
|
| 240 |
+
| 0.3281 | 3300 | 0.0003 | - |
|
| 241 |
+
| 0.3330 | 3350 | 0.0003 | - |
|
| 242 |
+
| 0.3380 | 3400 | 0.0003 | - |
|
| 243 |
+
| 0.3430 | 3450 | 0.0003 | - |
|
| 244 |
+
| 0.3479 | 3500 | 0.0003 | - |
|
| 245 |
+
| 0.3529 | 3550 | 0.0003 | - |
|
| 246 |
+
| 0.3579 | 3600 | 0.0003 | - |
|
| 247 |
+
| 0.3629 | 3650 | 0.0003 | - |
|
| 248 |
+
| 0.3678 | 3700 | 0.0003 | - |
|
| 249 |
+
| 0.3728 | 3750 | 0.0004 | - |
|
| 250 |
+
| 0.3778 | 3800 | 0.0004 | - |
|
| 251 |
+
| 0.3827 | 3850 | 0.0003 | - |
|
| 252 |
+
| 0.3877 | 3900 | 0.0003 | - |
|
| 253 |
+
| 0.3927 | 3950 | 0.0003 | - |
|
| 254 |
+
| 0.3977 | 4000 | 0.0003 | - |
|
| 255 |
+
| 0.4026 | 4050 | 0.0003 | - |
|
| 256 |
+
| 0.4076 | 4100 | 0.0003 | - |
|
| 257 |
+
| 0.4126 | 4150 | 0.0003 | - |
|
| 258 |
+
| 0.4175 | 4200 | 0.0003 | - |
|
| 259 |
+
| 0.4225 | 4250 | 0.0003 | - |
|
| 260 |
+
| 0.4275 | 4300 | 0.0003 | - |
|
| 261 |
+
| 0.4324 | 4350 | 0.0003 | - |
|
| 262 |
+
| 0.4374 | 4400 | 0.0002 | - |
|
| 263 |
+
| 0.4424 | 4450 | 0.0003 | - |
|
| 264 |
+
| 0.4474 | 4500 | 0.0003 | - |
|
| 265 |
+
| 0.4523 | 4550 | 0.0003 | - |
|
| 266 |
+
| 0.4573 | 4600 | 0.0003 | - |
|
| 267 |
+
| 0.4623 | 4650 | 0.0003 | - |
|
| 268 |
+
| 0.4672 | 4700 | 0.0002 | - |
|
| 269 |
+
| 0.4722 | 4750 | 0.0002 | - |
|
| 270 |
+
| 0.4772 | 4800 | 0.0003 | - |
|
| 271 |
+
| 0.4822 | 4850 | 0.0002 | - |
|
| 272 |
+
| 0.4871 | 4900 | 0.0002 | - |
|
| 273 |
+
| 0.4921 | 4950 | 0.0002 | - |
|
| 274 |
+
| 0.4971 | 5000 | 0.0003 | - |
|
| 275 |
+
| 0.5020 | 5050 | 0.0003 | - |
|
| 276 |
+
| 0.5070 | 5100 | 0.0002 | - |
|
| 277 |
+
| 0.5120 | 5150 | 0.0003 | - |
|
| 278 |
+
| 0.5169 | 5200 | 0.0002 | - |
|
| 279 |
+
| 0.5219 | 5250 | 0.0002 | - |
|
| 280 |
+
| 0.5269 | 5300 | 0.0002 | - |
|
| 281 |
+
| 0.5319 | 5350 | 0.0002 | - |
|
| 282 |
+
| 0.5368 | 5400 | 0.0003 | - |
|
| 283 |
+
| 0.5418 | 5450 | 0.0002 | - |
|
| 284 |
+
| 0.5468 | 5500 | 0.0002 | - |
|
| 285 |
+
| 0.5517 | 5550 | 0.0002 | - |
|
| 286 |
+
| 0.5567 | 5600 | 0.0002 | - |
|
| 287 |
+
| 0.5617 | 5650 | 0.0002 | - |
|
| 288 |
+
| 0.5667 | 5700 | 0.0002 | - |
|
| 289 |
+
| 0.5716 | 5750 | 0.0002 | - |
|
| 290 |
+
| 0.5766 | 5800 | 0.0002 | - |
|
| 291 |
+
| 0.5816 | 5850 | 0.0002 | - |
|
| 292 |
+
| 0.5865 | 5900 | 0.0002 | - |
|
| 293 |
+
| 0.5915 | 5950 | 0.0002 | - |
|
| 294 |
+
| 0.5965 | 6000 | 0.0002 | - |
|
| 295 |
+
| 0.6015 | 6050 | 0.0002 | - |
|
| 296 |
+
| 0.6064 | 6100 | 0.0002 | - |
|
| 297 |
+
| 0.6114 | 6150 | 0.0002 | - |
|
| 298 |
+
| 0.6164 | 6200 | 0.0002 | - |
|
| 299 |
+
| 0.6213 | 6250 | 0.0002 | - |
|
| 300 |
+
| 0.6263 | 6300 | 0.0002 | - |
|
| 301 |
+
| 0.6313 | 6350 | 0.0002 | - |
|
| 302 |
+
| 0.6362 | 6400 | 0.0002 | - |
|
| 303 |
+
| 0.6412 | 6450 | 0.0002 | - |
|
| 304 |
+
| 0.6462 | 6500 | 0.0002 | - |
|
| 305 |
+
| 0.6512 | 6550 | 0.0002 | - |
|
| 306 |
+
| 0.6561 | 6600 | 0.0002 | - |
|
| 307 |
+
| 0.6611 | 6650 | 0.0002 | - |
|
| 308 |
+
| 0.6661 | 6700 | 0.0002 | - |
|
| 309 |
+
| 0.6710 | 6750 | 0.0002 | - |
|
| 310 |
+
| 0.6760 | 6800 | 0.0002 | - |
|
| 311 |
+
| 0.6810 | 6850 | 0.0002 | - |
|
| 312 |
+
| 0.6860 | 6900 | 0.0002 | - |
|
| 313 |
+
| 0.6909 | 6950 | 0.0002 | - |
|
| 314 |
+
| 0.6959 | 7000 | 0.0002 | - |
|
| 315 |
+
| 0.7009 | 7050 | 0.0002 | - |
|
| 316 |
+
| 0.7058 | 7100 | 0.0002 | - |
|
| 317 |
+
| 0.7108 | 7150 | 0.0002 | - |
|
| 318 |
+
| 0.7158 | 7200 | 0.0002 | - |
|
| 319 |
+
| 0.7207 | 7250 | 0.0002 | - |
|
| 320 |
+
| 0.7257 | 7300 | 0.0002 | - |
|
| 321 |
+
| 0.7307 | 7350 | 0.0002 | - |
|
| 322 |
+
| 0.7357 | 7400 | 0.0002 | - |
|
| 323 |
+
| 0.7406 | 7450 | 0.0002 | - |
|
| 324 |
+
| 0.7456 | 7500 | 0.0002 | - |
|
| 325 |
+
| 0.7506 | 7550 | 0.0002 | - |
|
| 326 |
+
| 0.7555 | 7600 | 0.0002 | - |
|
| 327 |
+
| 0.7605 | 7650 | 0.0002 | - |
|
| 328 |
+
| 0.7655 | 7700 | 0.0248 | - |
|
| 329 |
+
| 0.7705 | 7750 | 0.0002 | - |
|
| 330 |
+
| 0.7754 | 7800 | 0.0002 | - |
|
| 331 |
+
| 0.7804 | 7850 | 0.0002 | - |
|
| 332 |
+
| 0.7854 | 7900 | 0.0002 | - |
|
| 333 |
+
| 0.7903 | 7950 | 0.0002 | - |
|
| 334 |
+
| 0.7953 | 8000 | 0.0002 | - |
|
| 335 |
+
| 0.8003 | 8050 | 0.0002 | - |
|
| 336 |
+
| 0.8052 | 8100 | 0.0002 | - |
|
| 337 |
+
| 0.8102 | 8150 | 0.0002 | - |
|
| 338 |
+
| 0.8152 | 8200 | 0.0002 | - |
|
| 339 |
+
| 0.8202 | 8250 | 0.0002 | - |
|
| 340 |
+
| 0.8251 | 8300 | 0.0002 | - |
|
| 341 |
+
| 0.8301 | 8350 | 0.0002 | - |
|
| 342 |
+
| 0.8351 | 8400 | 0.0002 | - |
|
| 343 |
+
| 0.8400 | 8450 | 0.0001 | - |
|
| 344 |
+
| 0.8450 | 8500 | 0.0002 | - |
|
| 345 |
+
| 0.8500 | 8550 | 0.0002 | - |
|
| 346 |
+
| 0.8550 | 8600 | 0.0001 | - |
|
| 347 |
+
| 0.8599 | 8650 | 0.0002 | - |
|
| 348 |
+
| 0.8649 | 8700 | 0.0002 | - |
|
| 349 |
+
| 0.8699 | 8750 | 0.0002 | - |
|
| 350 |
+
| 0.8748 | 8800 | 0.0002 | - |
|
| 351 |
+
| 0.8798 | 8850 | 0.0002 | - |
|
| 352 |
+
| 0.8848 | 8900 | 0.0002 | - |
|
| 353 |
+
| 0.8898 | 8950 | 0.0003 | - |
|
| 354 |
+
| 0.8947 | 9000 | 0.0002 | - |
|
| 355 |
+
| 0.8997 | 9050 | 0.0001 | - |
|
| 356 |
+
| 0.9047 | 9100 | 0.0002 | - |
|
| 357 |
+
| 0.9096 | 9150 | 0.0002 | - |
|
| 358 |
+
| 0.9146 | 9200 | 0.0002 | - |
|
| 359 |
+
| 0.9196 | 9250 | 0.0002 | - |
|
| 360 |
+
| 0.9245 | 9300 | 0.0002 | - |
|
| 361 |
+
| 0.9295 | 9350 | 0.0002 | - |
|
| 362 |
+
| 0.9345 | 9400 | 0.0002 | - |
|
| 363 |
+
| 0.9395 | 9450 | 0.0002 | - |
|
| 364 |
+
| 0.9444 | 9500 | 0.0002 | - |
|
| 365 |
+
| 0.9494 | 9550 | 0.0001 | - |
|
| 366 |
+
| 0.9544 | 9600 | 0.0001 | - |
|
| 367 |
+
| 0.9593 | 9650 | 0.0002 | - |
|
| 368 |
+
| 0.9643 | 9700 | 0.0002 | - |
|
| 369 |
+
| 0.9693 | 9750 | 0.0002 | - |
|
| 370 |
+
| 0.9743 | 9800 | 0.0001 | - |
|
| 371 |
+
| 0.9792 | 9850 | 0.0002 | - |
|
| 372 |
+
| 0.9842 | 9900 | 0.0002 | - |
|
| 373 |
+
| 0.9892 | 9950 | 0.0002 | - |
|
| 374 |
+
| 0.9941 | 10000 | 0.0002 | - |
|
| 375 |
+
| 0.9991 | 10050 | 0.0002 | - |
|
| 376 |
+
|
| 377 |
+
### Framework Versions
|
| 378 |
+
- Python: 3.10.12
|
| 379 |
+
- SetFit: 1.0.3
|
| 380 |
+
- Sentence Transformers: 2.3.1
|
| 381 |
+
- Transformers: 4.35.2
|
| 382 |
+
- PyTorch: 2.1.0+cu121
|
| 383 |
+
- Datasets: 2.17.0
|
| 384 |
+
- Tokenizers: 0.15.1
|
| 385 |
+
|
| 386 |
+
## Citation
|
| 387 |
+
|
| 388 |
+
### BibTeX
|
| 389 |
+
```bibtex
|
| 390 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 391 |
+
doi = {10.48550/ARXIV.2209.11055},
|
| 392 |
+
url = {https://arxiv.org/abs/2209.11055},
|
| 393 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 394 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 395 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
| 396 |
+
publisher = {arXiv},
|
| 397 |
+
year = {2022},
|
| 398 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
| 399 |
+
}
|
| 400 |
+
```
|
| 401 |
+
|
| 402 |
+
<!--
|
| 403 |
+
## Glossary
|
| 404 |
+
|
| 405 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 406 |
+
-->
|
| 407 |
+
|
| 408 |
+
<!--
|
| 409 |
+
## Model Card Authors
|
| 410 |
+
|
| 411 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 412 |
+
-->
|
| 413 |
+
|
| 414 |
+
<!--
|
| 415 |
+
## Model Card Contact
|
| 416 |
+
|
| 417 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 418 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "BAAI/bge-small-en-v1.5",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"classifier_dropout": null,
|
| 8 |
+
"hidden_act": "gelu",
|
| 9 |
+
"hidden_dropout_prob": 0.1,
|
| 10 |
+
"hidden_size": 384,
|
| 11 |
+
"id2label": {
|
| 12 |
+
"0": "LABEL_0"
|
| 13 |
+
},
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 1536,
|
| 16 |
+
"label2id": {
|
| 17 |
+
"LABEL_0": 0
|
| 18 |
+
},
|
| 19 |
+
"layer_norm_eps": 1e-12,
|
| 20 |
+
"max_position_embeddings": 512,
|
| 21 |
+
"model_type": "bert",
|
| 22 |
+
"num_attention_heads": 12,
|
| 23 |
+
"num_hidden_layers": 12,
|
| 24 |
+
"pad_token_id": 0,
|
| 25 |
+
"position_embedding_type": "absolute",
|
| 26 |
+
"torch_dtype": "float32",
|
| 27 |
+
"transformers_version": "4.35.2",
|
| 28 |
+
"type_vocab_size": 2,
|
| 29 |
+
"use_cache": true,
|
| 30 |
+
"vocab_size": 30522
|
| 31 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "2.2.2",
|
| 4 |
+
"transformers": "4.28.1",
|
| 5 |
+
"pytorch": "1.13.0+cu117"
|
| 6 |
+
}
|
| 7 |
+
}
|
config_setfit.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"normalize_embeddings": false,
|
| 3 |
+
"labels": [
|
| 4 |
+
"INDIVIDUAL",
|
| 5 |
+
"ORGANIZATIONAL"
|
| 6 |
+
]
|
| 7 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:31c4d4d8deb60272a52d9b38327125f74a7d2cbdcd65e2899eb9e1de9cc5fd38
|
| 3 |
+
size 133462128
|
model_head.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:a8e7dcbd80b5d881515a39bbab226e7af7127fa8843a0a020af0ffaeb8e145a8
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size 3919
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modules.json
ADDED
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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sentence_bert_config.json
ADDED
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{
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"max_seq_length": 512,
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"do_lower_case": true
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}
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special_tokens_map.json
ADDED
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@@ -0,0 +1,7 @@
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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{
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"added_tokens_decoder": {
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| 3 |
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"0": {
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| 4 |
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"content": "[PAD]",
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| 5 |
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"lstrip": false,
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| 6 |
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"normalized": false,
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| 7 |
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"rstrip": false,
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| 8 |
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"single_word": false,
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| 9 |
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"special": true
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| 10 |
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},
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"100": {
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| 12 |
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"content": "[UNK]",
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| 13 |
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"lstrip": false,
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| 14 |
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"normalized": false,
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| 15 |
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"rstrip": false,
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| 16 |
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"single_word": false,
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| 17 |
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"special": true
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| 18 |
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},
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| 19 |
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"101": {
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| 20 |
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"content": "[CLS]",
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| 21 |
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"lstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"rstrip": false,
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| 24 |
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"single_word": false,
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| 25 |
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"special": true
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| 26 |
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},
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| 27 |
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"102": {
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| 28 |
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"content": "[SEP]",
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| 29 |
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"lstrip": false,
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| 30 |
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"normalized": false,
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| 31 |
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"rstrip": false,
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| 32 |
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"single_word": false,
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| 33 |
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"special": true
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},
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"103": {
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| 36 |
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"content": "[MASK]",
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| 37 |
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"lstrip": false,
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| 38 |
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"normalized": false,
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| 39 |
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"rstrip": false,
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| 40 |
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"single_word": false,
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| 41 |
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"special": true
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| 42 |
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}
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| 43 |
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},
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| 44 |
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"clean_up_tokenization_spaces": true,
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| 45 |
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"cls_token": "[CLS]",
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| 46 |
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"do_basic_tokenize": true,
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| 47 |
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"do_lower_case": true,
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| 48 |
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"mask_token": "[MASK]",
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| 49 |
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"model_max_length": 512,
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| 50 |
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"never_split": null,
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| 51 |
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"pad_token": "[PAD]",
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| 52 |
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"sep_token": "[SEP]",
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| 53 |
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"strip_accents": null,
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| 54 |
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"tokenize_chinese_chars": true,
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| 55 |
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"tokenizer_class": "BertTokenizer",
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| 56 |
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"unk_token": "[UNK]"
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| 57 |
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}
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vocab.txt
ADDED
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