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roberta-base-mnli_MULTI

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
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update model card README.md

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@@ -3,19 +3,19 @@ license: mit
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: mnli_MULTI
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # mnli_MULTI
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  This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5379
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- - Acc: 0.8407
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  ## Model description
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@@ -40,30 +40,42 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Acc |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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- | 0.4798 | 0.17 | 2000 | 0.4797 | 0.8220 |
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- | 0.4483 | 0.33 | 4000 | 0.4576 | 0.8257 |
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- | 0.4282 | 0.5 | 6000 | 0.4628 | 0.8298 |
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- | 0.4333 | 0.67 | 8000 | 0.4426 | 0.8311 |
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- | 0.4251 | 0.83 | 10000 | 0.4449 | 0.8333 |
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- | 0.4154 | 1.0 | 12000 | 0.4518 | 0.8376 |
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- | 0.3064 | 1.17 | 14000 | 0.4845 | 0.8350 |
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- | 0.3072 | 1.33 | 16000 | 0.4803 | 0.8383 |
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- | 0.3144 | 1.5 | 18000 | 0.4824 | 0.8322 |
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- | 0.3025 | 1.66 | 20000 | 0.4634 | 0.8391 |
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- | 0.3063 | 1.83 | 22000 | 0.4625 | 0.8419 |
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- | 0.304 | 2.0 | 24000 | 0.4637 | 0.8401 |
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- | 0.2232 | 2.16 | 26000 | 0.5330 | 0.8434 |
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- | 0.2215 | 2.33 | 28000 | 0.5338 | 0.8374 |
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- | 0.2181 | 2.5 | 30000 | 0.5366 | 0.8393 |
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- | 0.216 | 2.66 | 32000 | 0.5333 | 0.8425 |
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- | 0.2118 | 2.83 | 34000 | 0.5293 | 0.8414 |
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- | 0.2128 | 3.0 | 36000 | 0.5379 | 0.8407 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  model-index:
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+ - name: roberta-base-mnli_MULTI
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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+ # roberta-base-mnli_MULTI
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  This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7607
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+ - Acc: 0.8310
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Acc |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 0.4798 | 0.17 | 2000 | 0.4810 | 0.8211 |
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+ | 0.4494 | 0.33 | 4000 | 0.4606 | 0.8268 |
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+ | 0.431 | 0.5 | 6000 | 0.4623 | 0.8301 |
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+ | 0.4371 | 0.67 | 8000 | 0.4437 | 0.8297 |
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+ | 0.4297 | 0.83 | 10000 | 0.4548 | 0.8313 |
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+ | 0.4214 | 1.0 | 12000 | 0.4566 | 0.8337 |
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+ | 0.3123 | 1.17 | 14000 | 0.4893 | 0.8323 |
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+ | 0.3158 | 1.33 | 16000 | 0.4861 | 0.8342 |
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+ | 0.324 | 1.5 | 18000 | 0.4812 | 0.8308 |
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+ | 0.3161 | 1.66 | 20000 | 0.4630 | 0.8365 |
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+ | 0.32 | 1.83 | 22000 | 0.4630 | 0.8366 |
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+ | 0.3195 | 2.0 | 24000 | 0.4681 | 0.8355 |
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+ | 0.2274 | 2.16 | 26000 | 0.5347 | 0.8407 |
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+ | 0.2311 | 2.33 | 28000 | 0.5650 | 0.8310 |
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+ | 0.2293 | 2.5 | 30000 | 0.5408 | 0.8355 |
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+ | 0.2296 | 2.66 | 32000 | 0.5207 | 0.8374 |
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+ | 0.2274 | 2.83 | 34000 | 0.5696 | 0.8353 |
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+ | 0.23 | 3.0 | 36000 | 0.5332 | 0.8366 |
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+ | 0.1686 | 3.16 | 38000 | 0.6275 | 0.8343 |
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+ | 0.1632 | 3.33 | 40000 | 0.6457 | 0.8349 |
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+ | 0.1686 | 3.5 | 42000 | 0.5965 | 0.8338 |
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+ | 0.1634 | 3.66 | 44000 | 0.6272 | 0.8342 |
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+ | 0.1656 | 3.83 | 46000 | 0.6541 | 0.8312 |
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+ | 0.162 | 4.0 | 48000 | 0.6408 | 0.8317 |
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+ | 0.1288 | 4.16 | 50000 | 0.7237 | 0.8349 |
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+ | 0.1275 | 4.33 | 52000 | 0.7558 | 0.8295 |
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+ | 0.1291 | 4.5 | 54000 | 0.7730 | 0.8306 |
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+ | 0.1261 | 4.66 | 56000 | 0.7524 | 0.8300 |
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+ | 0.1272 | 4.83 | 58000 | 0.7572 | 0.8317 |
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+ | 0.1242 | 4.99 | 60000 | 0.7607 | 0.8310 |
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  ### Framework versions