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combine-80-vsfc-xlm-r
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2271
- Precision: 0.9369
- Recall: 0.9394
- F1 Weighted: 0.9350
- F1 Macro: 0.8215
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Weighted | F1 Macro |
---|---|---|---|---|---|---|---|
1.058 | 0.08 | 25 | 0.8369 | 0.6858 | 0.6867 | 0.6569 | 0.4560 |
0.9176 | 0.16 | 50 | 0.5039 | 0.8362 | 0.8743 | 0.8541 | 0.5962 |
0.5966 | 0.25 | 75 | 0.4431 | 0.8507 | 0.8806 | 0.8609 | 0.6014 |
0.6479 | 0.33 | 100 | 0.5620 | 0.8416 | 0.8490 | 0.8449 | 0.6032 |
0.5151 | 0.41 | 125 | 0.3634 | 0.8689 | 0.9097 | 0.8883 | 0.6209 |
0.4465 | 0.49 | 150 | 0.3030 | 0.9070 | 0.9059 | 0.9063 | 0.7148 |
0.479 | 0.57 | 175 | 0.3010 | 0.9150 | 0.9242 | 0.9121 | 0.7101 |
0.3566 | 0.65 | 200 | 0.2962 | 0.9060 | 0.9236 | 0.9032 | 0.6388 |
0.3922 | 0.74 | 225 | 0.3038 | 0.8724 | 0.9084 | 0.8877 | 0.6198 |
0.3604 | 0.82 | 250 | 0.2561 | 0.9146 | 0.9255 | 0.9076 | 0.6647 |
0.3479 | 0.9 | 275 | 0.2598 | 0.9053 | 0.9071 | 0.8963 | 0.7053 |
0.3567 | 0.98 | 300 | 0.2927 | 0.9088 | 0.9191 | 0.8996 | 0.6444 |
0.3138 | 1.06 | 325 | 0.2448 | 0.9311 | 0.9337 | 0.9206 | 0.7201 |
0.3014 | 1.14 | 350 | 0.3007 | 0.9202 | 0.9261 | 0.9104 | 0.6875 |
0.3404 | 1.23 | 375 | 0.2653 | 0.9203 | 0.9248 | 0.9138 | 0.7314 |
0.2914 | 1.31 | 400 | 0.2585 | 0.9315 | 0.9330 | 0.9178 | 0.6999 |
0.3532 | 1.39 | 425 | 0.2592 | 0.9226 | 0.9236 | 0.9142 | 0.7486 |
0.289 | 1.47 | 450 | 0.2149 | 0.9389 | 0.9419 | 0.9376 | 0.8217 |
0.2433 | 1.55 | 475 | 0.2335 | 0.9303 | 0.9343 | 0.9311 | 0.8148 |
0.2934 | 1.63 | 500 | 0.2241 | 0.9344 | 0.9318 | 0.9329 | 0.8148 |
0.2717 | 1.72 | 525 | 0.2238 | 0.9342 | 0.9381 | 0.9337 | 0.8123 |
0.2696 | 1.8 | 550 | 0.2396 | 0.9347 | 0.9381 | 0.9351 | 0.8277 |
0.2654 | 1.88 | 575 | 0.2508 | 0.9335 | 0.9356 | 0.9267 | 0.7679 |
0.2687 | 1.96 | 600 | 0.2119 | 0.9344 | 0.9381 | 0.9350 | 0.8228 |
0.304 | 2.04 | 625 | 0.2453 | 0.9290 | 0.9311 | 0.9237 | 0.7795 |
0.2593 | 2.12 | 650 | 0.2066 | 0.9377 | 0.9400 | 0.9384 | 0.8444 |
0.2372 | 2.21 | 675 | 0.2240 | 0.9378 | 0.9406 | 0.9362 | 0.8225 |
0.2253 | 2.29 | 700 | 0.2325 | 0.9404 | 0.9419 | 0.9370 | 0.8237 |
0.2539 | 2.37 | 725 | 0.2218 | 0.9381 | 0.9400 | 0.9369 | 0.8400 |
0.2434 | 2.45 | 750 | 0.2260 | 0.9377 | 0.9394 | 0.9384 | 0.8404 |
0.2047 | 2.53 | 775 | 0.2320 | 0.9381 | 0.9394 | 0.9328 | 0.8009 |
0.2331 | 2.61 | 800 | 0.2094 | 0.9367 | 0.9394 | 0.9374 | 0.8325 |
0.2152 | 2.7 | 825 | 0.2365 | 0.9366 | 0.9387 | 0.9337 | 0.8157 |
0.2306 | 2.78 | 850 | 0.2675 | 0.9298 | 0.9324 | 0.9288 | 0.8219 |
0.2537 | 2.86 | 875 | 0.2271 | 0.9369 | 0.9394 | 0.9350 | 0.8215 |
Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2