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TTC4900Model

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +14 -14
  2. model.safetensors +1 -1
README.md CHANGED
@@ -9,22 +9,22 @@ metrics:
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  - precision
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  - recall
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  model-index:
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- - name: fintunned-v2-roberta
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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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- # fintunned-v2-roberta
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2012
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- - Accuracy: 0.95
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- - F1: 0.9504
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- - Precision: 0.9517
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- - Recall: 0.9498
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  ## Model description
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@@ -57,17 +57,17 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 2.3929 | 0.45 | 50 | 2.2723 | 0.2773 | 0.1892 | 0.2335 | 0.2947 |
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- | 1.2165 | 0.91 | 100 | 0.4612 | 0.8818 | 0.8839 | 0.8978 | 0.8825 |
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- | 0.3732 | 1.36 | 150 | 0.3472 | 0.9045 | 0.9058 | 0.9092 | 0.9060 |
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- | 0.3306 | 1.82 | 200 | 0.3077 | 0.9227 | 0.9249 | 0.9267 | 0.9250 |
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- | 0.2537 | 2.27 | 250 | 0.2419 | 0.9273 | 0.9281 | 0.9290 | 0.9291 |
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- | 0.0997 | 2.73 | 300 | 0.2012 | 0.95 | 0.9504 | 0.9517 | 0.9498 |
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu121
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- - Datasets 2.16.1
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  - Tokenizers 0.15.1
 
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  - precision
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  - recall
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  model-index:
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+ - name: fintunned-v2-roberta_GA
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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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+ # fintunned-v2-roberta_GA
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1635
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+ - Accuracy: 0.9523
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+ - F1: 0.9527
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+ - Precision: 0.9534
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+ - Recall: 0.9523
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 2.3896 | 0.45 | 50 | 2.2632 | 0.325 | 0.2696 | 0.4504 | 0.3447 |
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+ | 1.2481 | 0.91 | 100 | 0.4536 | 0.8841 | 0.8873 | 0.8940 | 0.8892 |
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+ | 0.3487 | 1.36 | 150 | 0.2978 | 0.9136 | 0.9161 | 0.9186 | 0.9167 |
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+ | 0.2618 | 1.82 | 200 | 0.2472 | 0.9295 | 0.9319 | 0.9362 | 0.9313 |
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+ | 0.2223 | 2.27 | 250 | 0.1872 | 0.9409 | 0.9415 | 0.9445 | 0.9408 |
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+ | 0.076 | 2.73 | 300 | 0.1635 | 0.9523 | 0.9527 | 0.9534 | 0.9523 |
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.0
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  - Tokenizers 0.15.1
model.safetensors CHANGED
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