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gg-bert-base-uncased

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
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  1. README.md +38 -35
  2. model.safetensors +1 -1
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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  license: mit
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- base_model: FacebookAI/roberta-large
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -9,24 +9,22 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: absa-train-service-roberta-large
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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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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cunho2803032003/absa-1721959498.2993438/runs/tad25dun)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cunho2803032003/absa-1721959940.7872202/runs/bsprskdy)
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- # absa-train-service-roberta-large
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- This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8683
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- - Accuracy: 0.7424
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- - Precision: 0.7345
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- - Recall: 0.7367
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- - F1: 0.7302
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  ## Model description
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@@ -52,37 +50,42 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 20
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 2.2255 | 1.0 | 469 | 2.0677 | 0.3296 | 0.1937 | 0.3250 | 0.2297 |
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- | 1.8236 | 2.0 | 938 | 1.7061 | 0.504 | 0.5413 | 0.4914 | 0.4567 |
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- | 1.5384 | 3.0 | 1407 | 1.4381 | 0.552 | 0.5944 | 0.5549 | 0.5196 |
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- | 1.4301 | 4.0 | 1876 | 1.3316 | 0.5984 | 0.6000 | 0.5990 | 0.5618 |
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- | 1.3776 | 5.0 | 2345 | 1.1645 | 0.6576 | 0.6817 | 0.6491 | 0.6332 |
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- | 1.2078 | 6.0 | 2814 | 1.0967 | 0.6448 | 0.7035 | 0.6348 | 0.6110 |
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- | 1.2535 | 7.0 | 3283 | 1.0565 | 0.7008 | 0.7467 | 0.6967 | 0.7066 |
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- | 1.2921 | 8.0 | 3752 | 1.0049 | 0.6976 | 0.7013 | 0.6884 | 0.6813 |
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- | 1.178 | 9.0 | 4221 | 1.0438 | 0.648 | 0.7746 | 0.6423 | 0.6387 |
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- | 1.2324 | 10.0 | 4690 | 1.0203 | 0.6896 | 0.7096 | 0.6831 | 0.6704 |
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- | 1.1899 | 11.0 | 5159 | 1.0193 | 0.6864 | 0.7391 | 0.6819 | 0.6834 |
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- | 1.1515 | 12.0 | 5628 | 0.9722 | 0.6944 | 0.7164 | 0.6924 | 0.6860 |
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- | 1.1604 | 13.0 | 6097 | 0.9372 | 0.7312 | 0.7543 | 0.7311 | 0.7259 |
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- | 1.1229 | 14.0 | 6566 | 0.9265 | 0.72 | 0.7278 | 0.7139 | 0.7147 |
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- | 1.1459 | 15.0 | 7035 | 0.8896 | 0.7376 | 0.7264 | 0.7323 | 0.7183 |
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- | 1.1281 | 16.0 | 7504 | 0.9074 | 0.7152 | 0.7107 | 0.7087 | 0.7012 |
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- | 1.1794 | 17.0 | 7973 | 0.8914 | 0.7424 | 0.7293 | 0.7354 | 0.7266 |
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- | 1.1101 | 18.0 | 8442 | 0.8707 | 0.7216 | 0.7161 | 0.7141 | 0.7059 |
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- | 1.1215 | 19.0 | 8911 | 0.8656 | 0.7408 | 0.7322 | 0.7348 | 0.7274 |
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- | 1.0483 | 20.0 | 9380 | 0.8683 | 0.7424 | 0.7345 | 0.7367 | 0.7302 |
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.43.2
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  - Pytorch 2.3.1+cu121
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  - Datasets 2.20.0
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  - Tokenizers 0.19.1
 
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  ---
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  license: mit
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+ base_model: FacebookAI/xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  - recall
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  - f1
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  model-index:
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+ - name: gg-bert-base-uncased
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  results: []
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  ---
15
 
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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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+ # gg-bert-base-uncased
 
 
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+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7791
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+ - Accuracy: 0.752
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+ - Precision: 0.7388
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+ - Recall: 0.7570
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+ - F1: 0.7396
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  ## Model description
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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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  - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 25
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.2541 | 1.0 | 469 | 2.2063 | 0.1488 | 0.2698 | 0.1552 | 0.1036 |
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+ | 1.8967 | 2.0 | 938 | 1.8773 | 0.5168 | 0.5264 | 0.5331 | 0.4788 |
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+ | 1.5747 | 3.0 | 1407 | 1.5546 | 0.5984 | 0.6125 | 0.6118 | 0.5636 |
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+ | 1.4206 | 4.0 | 1876 | 1.3029 | 0.6528 | 0.6732 | 0.6666 | 0.6224 |
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+ | 1.2804 | 5.0 | 2345 | 1.1876 | 0.6928 | 0.6972 | 0.6989 | 0.6844 |
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+ | 1.1587 | 6.0 | 2814 | 1.0644 | 0.7136 | 0.7105 | 0.7136 | 0.6858 |
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+ | 1.1589 | 7.0 | 3283 | 0.9883 | 0.7216 | 0.7173 | 0.7261 | 0.7031 |
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+ | 1.0745 | 8.0 | 3752 | 0.9485 | 0.728 | 0.7151 | 0.7318 | 0.7195 |
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+ | 1.0348 | 9.0 | 4221 | 0.9278 | 0.7328 | 0.7306 | 0.7372 | 0.7166 |
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+ | 1.0019 | 10.0 | 4690 | 0.9114 | 0.72 | 0.7316 | 0.7231 | 0.7006 |
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+ | 1.0204 | 11.0 | 5159 | 0.8967 | 0.7152 | 0.7187 | 0.7215 | 0.6895 |
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+ | 1.0651 | 12.0 | 5628 | 0.8574 | 0.7424 | 0.7446 | 0.7474 | 0.7327 |
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+ | 0.9841 | 13.0 | 6097 | 0.8461 | 0.7328 | 0.7495 | 0.7370 | 0.7076 |
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+ | 0.9794 | 14.0 | 6566 | 0.8510 | 0.7248 | 0.7157 | 0.7319 | 0.7022 |
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+ | 1.0242 | 15.0 | 7035 | 0.8127 | 0.7264 | 0.7112 | 0.7300 | 0.6998 |
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+ | 0.9614 | 16.0 | 7504 | 0.8146 | 0.7312 | 0.7210 | 0.7376 | 0.7149 |
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+ | 0.9358 | 17.0 | 7973 | 0.8288 | 0.736 | 0.7487 | 0.7439 | 0.7275 |
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+ | 0.9719 | 18.0 | 8442 | 0.7958 | 0.7488 | 0.7403 | 0.7530 | 0.7349 |
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+ | 0.9159 | 19.0 | 8911 | 0.7973 | 0.7472 | 0.7388 | 0.7522 | 0.7357 |
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+ | 0.9824 | 20.0 | 9380 | 0.7921 | 0.7504 | 0.7439 | 0.7562 | 0.7363 |
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+ | 1.0215 | 21.0 | 9849 | 0.7831 | 0.7536 | 0.7415 | 0.7586 | 0.7392 |
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+ | 0.9191 | 22.0 | 10318 | 0.7780 | 0.7504 | 0.7387 | 0.7554 | 0.7399 |
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+ | 0.9087 | 23.0 | 10787 | 0.7843 | 0.7472 | 0.7352 | 0.7536 | 0.7345 |
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+ | 0.9198 | 24.0 | 11256 | 0.7793 | 0.7504 | 0.7358 | 0.7554 | 0.7374 |
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+ | 0.9162 | 25.0 | 11725 | 0.7791 | 0.752 | 0.7388 | 0.7570 | 0.7396 |
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  ### Framework versions
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+ - Transformers 4.43.3
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  - Pytorch 2.3.1+cu121
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  - Datasets 2.20.0
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  - Tokenizers 0.19.1
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