File size: 20,117 Bytes
c59103e 2437f2b c59103e 3193dce c59103e 3193dce c59103e fbb7ba7 c59103e 3193dce c59103e fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 c59103e ab74508 c59103e ab74508 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 5136d13 fbb7ba7 ab74508 5136d13 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 ab74508 fbb7ba7 ab74508 fbb7ba7 5136d13 fbb7ba7 ab74508 c59103e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_agent
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: agent_action_class
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8242530755711776
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Action_agent
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the agent_action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9962
- Accuracy: 0.8243
- Confusion Matrix: [[39, 3, 0, 0, 2, 1, 0, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 4, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [2, 9, 0, 0, 0, 0, 9, 1, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]]
- Classification Report: precision recall f1-score support
0 0.7800 0.7500 0.7647 52
1 0.8028 0.9500 0.8702 60
2 0.7600 0.7451 0.7525 51
3 0.8298 0.7091 0.7647 55
4 0.9091 0.8929 0.9009 56
5 0.8302 0.7857 0.8073 56
6 0.8333 0.8730 0.8527 63
7 0.8667 0.9286 0.8966 56
8 0.8667 0.6500 0.7429 60
9 0.7778 0.9333 0.8485 60
accuracy 0.8243 569
macro avg 0.8256 0.8218 0.8201 569
weighted avg 0.8264 0.8243 0.8216 569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 2.1982 | 0.75 | 100 | 2.1583 | 0.4851 | [[2, 3, 2, 1, 3, 1, 7, 15, 10, 8], [1, 52, 0, 0, 2, 0, 0, 2, 2, 1], [1, 0, 15, 0, 5, 0, 3, 23, 3, 1], [2, 1, 8, 12, 5, 0, 6, 6, 1, 14], [0, 2, 9, 1, 30, 2, 2, 3, 2, 5], [0, 2, 6, 2, 5, 16, 2, 16, 4, 3], [0, 7, 0, 1, 5, 2, 27, 1, 12, 8], [0, 0, 1, 0, 0, 0, 1, 54, 0, 0], [0, 11, 1, 0, 3, 2, 5, 7, 31, 0], [0, 3, 4, 1, 4, 1, 1, 6, 3, 37]] | precision recall f1-score support
0 0.3333 0.0385 0.0690 52
1 0.6420 0.8667 0.7376 60
2 0.3261 0.2941 0.3093 51
3 0.6667 0.2182 0.3288 55
4 0.4839 0.5357 0.5085 56
5 0.6667 0.2857 0.4000 56
6 0.5000 0.4286 0.4615 63
7 0.4060 0.9643 0.5714 56
8 0.4559 0.5167 0.4844 60
9 0.4805 0.6167 0.5401 60
accuracy 0.4851 569
macro avg 0.4961 0.4765 0.4411 569
weighted avg 0.4991 0.4851 0.4484 569
|
| 1.988 | 1.49 | 200 | 1.9350 | 0.6257 | [[11, 6, 2, 0, 7, 1, 3, 10, 7, 5], [0, 58, 0, 0, 1, 0, 0, 0, 1, 0], [1, 1, 19, 0, 4, 1, 1, 24, 0, 0], [1, 1, 5, 16, 3, 0, 6, 7, 0, 16], [1, 1, 1, 0, 50, 0, 2, 0, 0, 1], [1, 0, 11, 0, 6, 25, 0, 11, 0, 2], [2, 8, 1, 1, 3, 1, 38, 2, 5, 2], [0, 0, 1, 0, 0, 0, 0, 55, 0, 0], [1, 12, 0, 0, 1, 1, 5, 6, 34, 0], [1, 0, 2, 3, 2, 0, 0, 2, 0, 50]] | precision recall f1-score support
0 0.5789 0.2115 0.3099 52
1 0.6667 0.9667 0.7891 60
2 0.4524 0.3725 0.4086 51
3 0.8000 0.2909 0.4267 55
4 0.6494 0.8929 0.7519 56
5 0.8621 0.4464 0.5882 56
6 0.6909 0.6032 0.6441 63
7 0.4701 0.9821 0.6358 56
8 0.7234 0.5667 0.6355 60
9 0.6579 0.8333 0.7353 60
accuracy 0.6257 569
macro avg 0.6552 0.6166 0.5925 569
weighted avg 0.6583 0.6257 0.5997 569
|
| 1.7347 | 2.24 | 300 | 1.6937 | 0.7223 | [[28, 4, 2, 1, 4, 1, 1, 1, 6, 4], [0, 58, 0, 0, 0, 0, 1, 0, 1, 0], [3, 0, 28, 0, 1, 1, 1, 16, 0, 1], [2, 2, 2, 29, 1, 0, 2, 2, 0, 15], [2, 1, 1, 0, 49, 0, 1, 0, 0, 2], [1, 0, 6, 0, 3, 35, 1, 8, 0, 2], [4, 5, 1, 1, 1, 0, 38, 1, 10, 2], [0, 0, 0, 0, 0, 0, 0, 56, 0, 0], [6, 11, 0, 0, 1, 0, 5, 2, 35, 0], [0, 0, 2, 2, 0, 0, 0, 1, 0, 55]] | precision recall f1-score support
0 0.6087 0.5385 0.5714 52
1 0.7160 0.9667 0.8227 60
2 0.6667 0.5490 0.6022 51
3 0.8788 0.5273 0.6591 55
4 0.8167 0.8750 0.8448 56
5 0.9459 0.6250 0.7527 56
6 0.7600 0.6032 0.6726 63
7 0.6437 1.0000 0.7832 56
8 0.6731 0.5833 0.6250 60
9 0.6790 0.9167 0.7801 60
accuracy 0.7223 569
macro avg 0.7389 0.7185 0.7114 569
weighted avg 0.7394 0.7223 0.7136 569
|
| 1.5713 | 2.99 | 400 | 1.4857 | 0.7434 | [[26, 6, 2, 1, 5, 1, 0, 2, 5, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 29, 1, 2, 2, 2, 13, 0, 0], [3, 1, 4, 32, 1, 1, 0, 1, 0, 12], [1, 1, 1, 0, 49, 0, 1, 0, 0, 3], [1, 0, 6, 0, 4, 41, 0, 2, 0, 2], [3, 5, 1, 0, 1, 0, 42, 0, 8, 3], [0, 0, 0, 1, 0, 0, 0, 55, 0, 0], [4, 11, 0, 0, 0, 0, 8, 2, 35, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
0 0.6500 0.5000 0.5652 52
1 0.7037 0.9500 0.8085 60
2 0.6444 0.5686 0.6042 51
3 0.9143 0.5818 0.7111 55
4 0.7903 0.8750 0.8305 56
5 0.9111 0.7321 0.8119 56
6 0.7778 0.6667 0.7179 63
7 0.7237 0.9821 0.8333 56
8 0.7143 0.5833 0.6422 60
9 0.6951 0.9500 0.8028 60
accuracy 0.7434 569
macro avg 0.7525 0.7390 0.7328 569
weighted avg 0.7532 0.7434 0.7353 569
|
| 1.3821 | 3.73 | 500 | 1.3477 | 0.7575 | [[30, 4, 0, 3, 4, 1, 0, 2, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 30, 4, 1, 2, 1, 10, 0, 1], [3, 2, 2, 27, 0, 1, 0, 2, 0, 18], [1, 1, 1, 0, 49, 0, 1, 0, 0, 3], [1, 0, 5, 0, 1, 44, 1, 1, 0, 3], [4, 0, 1, 1, 1, 0, 49, 0, 3, 4], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [3, 11, 0, 0, 0, 0, 10, 2, 34, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 58]] | precision recall f1-score support
0 0.6818 0.5769 0.6250 52
1 0.7600 0.9500 0.8444 60
2 0.7143 0.5882 0.6452 51
3 0.7500 0.4909 0.5934 55
4 0.8750 0.8750 0.8750 56
5 0.9167 0.7857 0.8462 56
6 0.7778 0.7778 0.7778 63
7 0.7465 0.9464 0.8346 56
8 0.8095 0.5667 0.6667 60
9 0.6304 0.9667 0.7632 60
accuracy 0.7575 569
macro avg 0.7662 0.7524 0.7471 569
weighted avg 0.7667 0.7575 0.7498 569
|
| 1.3065 | 4.48 | 600 | 1.2437 | 0.7856 | [[33, 4, 0, 1, 3, 1, 0, 2, 4, 4], [0, 56, 0, 0, 0, 0, 1, 0, 2, 1], [1, 0, 29, 5, 1, 2, 1, 12, 0, 0], [2, 1, 1, 36, 0, 3, 0, 2, 0, 10], [1, 1, 1, 1, 50, 0, 0, 0, 0, 2], [1, 0, 4, 1, 1, 42, 1, 4, 0, 2], [3, 0, 0, 0, 1, 0, 53, 0, 3, 3], [0, 0, 0, 1, 0, 0, 0, 55, 0, 0], [4, 9, 0, 0, 0, 0, 9, 1, 37, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7333 0.6346 0.6804 52
1 0.7887 0.9333 0.8550 60
2 0.8286 0.5686 0.6744 51
3 0.7660 0.6545 0.7059 55
4 0.8929 0.8929 0.8929 56
5 0.8571 0.7500 0.8000 56
6 0.8154 0.8413 0.8281 63
7 0.7143 0.9821 0.8271 56
8 0.8043 0.6167 0.6981 60
9 0.7179 0.9333 0.8116 60
accuracy 0.7856 569
macro avg 0.7919 0.7807 0.7773 569
weighted avg 0.7918 0.7856 0.7799 569
|
| 1.2329 | 5.22 | 700 | 1.1645 | 0.7909 | [[34, 4, 0, 1, 3, 1, 0, 1, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 33, 5, 1, 3, 1, 7, 0, 0], [3, 1, 1, 31, 1, 2, 0, 1, 0, 15], [1, 1, 1, 1, 50, 0, 0, 0, 0, 2], [1, 0, 7, 1, 2, 43, 0, 0, 0, 2], [2, 0, 0, 0, 1, 0, 56, 0, 1, 3], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [2, 11, 0, 0, 0, 0, 10, 1, 36, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 57]] | precision recall f1-score support
0 0.7727 0.6538 0.7083 52
1 0.7703 0.9500 0.8507 60
2 0.7500 0.6471 0.6947 51
3 0.7561 0.5636 0.6458 55
4 0.8621 0.8929 0.8772 56
5 0.8600 0.7679 0.8113 56
6 0.8235 0.8889 0.8550 63
7 0.8281 0.9464 0.8833 56
8 0.8571 0.6000 0.7059 60
9 0.6786 0.9500 0.7917 60
accuracy 0.7909 569
macro avg 0.7959 0.7861 0.7824 569
weighted avg 0.7963 0.7909 0.7848 569
|
| 1.1736 | 5.97 | 800 | 1.1159 | 0.7891 | [[35, 4, 0, 0, 2, 1, 1, 1, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 35, 2, 1, 3, 1, 7, 0, 0], [3, 1, 0, 34, 0, 3, 0, 1, 0, 13], [1, 1, 2, 1, 49, 0, 0, 0, 0, 2], [1, 0, 7, 1, 1, 43, 1, 0, 0, 2], [3, 0, 0, 0, 1, 0, 51, 0, 4, 4], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [4, 10, 0, 0, 0, 0, 8, 1, 37, 0], [0, 0, 0, 3, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7143 0.6731 0.6931 52
1 0.7808 0.9500 0.8571 60
2 0.7447 0.6863 0.7143 51
3 0.8095 0.6182 0.7010 55
4 0.9074 0.8750 0.8909 56
5 0.8600 0.7679 0.8113 56
6 0.8095 0.8095 0.8095 63
7 0.8254 0.9286 0.8739 56
8 0.8043 0.6167 0.6981 60
9 0.6829 0.9333 0.7887 60
accuracy 0.7891 569
macro avg 0.7939 0.7858 0.7838 569
weighted avg 0.7942 0.7891 0.7855 569
|
| 1.1396 | 6.72 | 900 | 1.0749 | 0.8067 | [[39, 3, 0, 0, 1, 1, 0, 2, 3, 3], [1, 56, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 38, 1, 1, 3, 0, 6, 0, 0], [3, 1, 1, 33, 0, 3, 0, 1, 0, 13], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 0, 1, 0, 53, 0, 2, 4], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 9, 0, 0, 0, 0, 8, 1, 37, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 57]] | precision recall f1-score support
0 0.7222 0.7500 0.7358 52
1 0.8000 0.9333 0.8615 60
2 0.7451 0.7451 0.7451 51
3 0.8684 0.6000 0.7097 55
4 0.9259 0.8929 0.9091 56
5 0.8462 0.7857 0.8148 56
6 0.8413 0.8413 0.8413 63
7 0.8254 0.9286 0.8739 56
8 0.8605 0.6167 0.7184 60
9 0.7037 0.9500 0.8085 60
accuracy 0.8067 569
macro avg 0.8139 0.8044 0.8018 569
weighted avg 0.8148 0.8067 0.8033 569
|
| 1.0577 | 7.46 | 1000 | 1.0399 | 0.8155 | [[37, 3, 0, 0, 1, 1, 1, 2, 4, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 4, 1, 4, 0, 3, 0, 0], [3, 1, 0, 40, 0, 3, 0, 1, 0, 7], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 6, 1, 1, 45, 1, 0, 0, 2], [3, 0, 0, 2, 1, 0, 53, 0, 2, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [3, 9, 0, 0, 0, 0, 9, 1, 38, 0], [0, 0, 0, 4, 0, 1, 0, 1, 0, 54]] | precision recall f1-score support
0 0.7708 0.7115 0.7400 52
1 0.8028 0.9500 0.8702 60
2 0.7755 0.7451 0.7600 51
3 0.7547 0.7273 0.7407 55
4 0.9259 0.8929 0.9091 56
5 0.8333 0.8036 0.8182 56
6 0.8154 0.8413 0.8281 63
7 0.8667 0.9286 0.8966 56
8 0.8444 0.6333 0.7238 60
9 0.7714 0.9000 0.8308 60
accuracy 0.8155 569
macro avg 0.8161 0.8134 0.8117 569
weighted avg 0.8167 0.8155 0.8130 569
|
| 0.9935 | 8.21 | 1100 | 1.0205 | 0.8190 | [[38, 4, 0, 0, 1, 1, 0, 2, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 3, 0, 6, 0, 0], [3, 1, 0, 38, 0, 3, 0, 1, 0, 9], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 2, 44, 0, 0, 0, 2], [3, 0, 0, 2, 1, 0, 54, 0, 2, 1], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [2, 10, 0, 0, 0, 0, 9, 1, 38, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7917 0.7308 0.7600 52
1 0.7808 0.9500 0.8571 60
2 0.7755 0.7451 0.7600 51
3 0.8085 0.6909 0.7451 55
4 0.9091 0.8929 0.9009 56
5 0.8462 0.7857 0.8148 56
6 0.8438 0.8571 0.8504 63
7 0.8281 0.9464 0.8833 56
8 0.8636 0.6333 0.7308 60
9 0.7671 0.9333 0.8421 60
accuracy 0.8190 569
macro avg 0.8214 0.8166 0.8145 569
weighted avg 0.8220 0.8190 0.8158 569
|
| 1.1058 | 8.96 | 1200 | 1.0022 | 0.8225 | [[38, 3, 0, 0, 2, 1, 1, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 37, 2, 1, 5, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 6, 1, 1, 45, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [3, 9, 0, 0, 0, 0, 9, 0, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7600 0.7308 0.7451 52
1 0.8028 0.9500 0.8702 60
2 0.7708 0.7255 0.7475 51
3 0.8298 0.7091 0.7647 55
4 0.9091 0.8929 0.9009 56
5 0.8182 0.8036 0.8108 56
6 0.8209 0.8730 0.8462 63
7 0.8814 0.9286 0.9043 56
8 0.8667 0.6500 0.7429 60
9 0.7778 0.9333 0.8485 60
accuracy 0.8225 569
macro avg 0.8237 0.8197 0.8181 569
weighted avg 0.8244 0.8225 0.8197 569
|
| 1.0422 | 9.7 | 1300 | 0.9962 | 0.8243 | [[39, 3, 0, 0, 2, 1, 0, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 4, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [2, 9, 0, 0, 0, 0, 9, 1, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7800 0.7500 0.7647 52
1 0.8028 0.9500 0.8702 60
2 0.7600 0.7451 0.7525 51
3 0.8298 0.7091 0.7647 55
4 0.9091 0.8929 0.9009 56
5 0.8302 0.7857 0.8073 56
6 0.8333 0.8730 0.8527 63
7 0.8667 0.9286 0.8966 56
8 0.8667 0.6500 0.7429 60
9 0.7778 0.9333 0.8485 60
accuracy 0.8243 569
macro avg 0.8256 0.8218 0.8201 569
weighted avg 0.8264 0.8243 0.8216 569
|
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|