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---
library_name: transformers
language:
- en
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
datasets:
- arielcerdap/TimeStamped
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Disfluency Labeling - Ariel Cerda
results: []
---
<!-- 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. -->
# Disfluency Labeling - Ariel Cerda
This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the TimeStamped dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6249
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9075
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2995 | 0.2194 | 500 | 0.3309 | 0.9467 | 0.3143 | 0.4720 | 0.9365 |
| 0.354 | 0.4388 | 1000 | 0.2799 | 0.9079 | 0.3216 | 0.4750 | 0.9366 |
| 0.3717 | 0.6582 | 1500 | 0.2715 | 0.9458 | 0.3351 | 0.4948 | 0.9381 |
| 0.3362 | 0.8776 | 2000 | 0.3026 | 0.9153 | 0.3195 | 0.4737 | 0.9363 |
| 0.2668 | 1.0970 | 2500 | 0.3130 | 0.9519 | 0.3320 | 0.4923 | 0.9376 |
| 0.3311 | 1.3164 | 3000 | 0.2815 | 0.9687 | 0.3299 | 0.4922 | 0.9379 |
| 0.3345 | 1.5358 | 3500 | 0.3048 | 0.9976 | 0.2506 | 0.4006 | 0.9307 |
| 0.2945 | 1.7552 | 4000 | 0.2890 | 0.9621 | 0.3326 | 0.4943 | 0.9378 |
| 0.2648 | 1.9746 | 4500 | 0.2850 | 0.9740 | 0.3311 | 0.4942 | 0.9380 |
| 0.3272 | 2.1939 | 5000 | 0.2827 | 0.9657 | 0.3430 | 0.5062 | 0.9388 |
| 0.3161 | 2.4133 | 5500 | 0.2759 | 0.9237 | 0.3357 | 0.4924 | 0.9367 |
| 0.2687 | 2.6327 | 6000 | 0.2891 | 0.9757 | 0.3308 | 0.4941 | 0.9381 |
| 0.2948 | 2.8521 | 6500 | 0.2872 | 0.9784 | 0.3177 | 0.4796 | 0.9368 |
| 0.2608 | 3.0715 | 7000 | 0.2901 | 0.8284 | 0.3445 | 0.4866 | 0.9338 |
| 0.2947 | 3.2909 | 7500 | 0.2829 | 0.9572 | 0.3341 | 0.4954 | 0.9379 |
| 0.2939 | 3.5103 | 8000 | 0.2814 | 0.9702 | 0.3277 | 0.4900 | 0.9377 |
| 0.2581 | 3.7297 | 8500 | 0.2764 | 0.9757 | 0.3311 | 0.4944 | 0.9381 |
| 0.3108 | 3.9491 | 9000 | 0.2809 | 0.9721 | 0.3293 | 0.4919 | 0.9379 |
| 0.2929 | 4.1685 | 9500 | 0.2874 | 0.9737 | 0.3274 | 0.4901 | 0.9377 |
| 0.2939 | 4.3879 | 10000 | 0.2760 | 0.9689 | 0.3323 | 0.4949 | 0.9381 |
| 0.3173 | 4.6073 | 10500 | 0.2784 | 0.9722 | 0.3311 | 0.4940 | 0.9381 |
| 0.2784 | 4.8267 | 11000 | 0.2825 | 0.9709 | 0.3360 | 0.4992 | 0.9384 |
| 0.2593 | 5.0461 | 11500 | 0.2775 | 0.9724 | 0.3335 | 0.4967 | 0.9383 |
| 0.2507 | 5.2655 | 12000 | 0.2985 | 0.9708 | 0.3348 | 0.4978 | 0.9383 |
| 0.2707 | 5.4849 | 12500 | 0.2805 | 0.9714 | 0.3421 | 0.5060 | 0.9389 |
| 0.2775 | 5.7043 | 13000 | 0.2757 | 0.9697 | 0.3421 | 0.5057 | 0.9390 |
| 0.5178 | 5.9237 | 13500 | 0.4682 | 0.9052 | 0.0845 | 0.1545 | 0.9151 |
| 0.3553 | 6.1430 | 14000 | 0.3657 | 0.9574 | 0.1988 | 0.3292 | 0.9257 |
| 0.3496 | 6.3624 | 14500 | 0.3986 | 0.9565 | 0.1945 | 0.3233 | 0.9253 |
| 0.3452 | 6.5818 | 15000 | 0.4337 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.3931 | 6.8012 | 15500 | 0.5834 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.4035 | 7.0206 | 16000 | 0.5584 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.3831 | 7.2400 | 16500 | 0.5585 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.2817 | 7.4594 | 17000 | 0.5946 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.3641 | 7.6788 | 17500 | 0.6069 | 0.0 | 0.0 | 0.0 | 0.9075 |
| 0.3866 | 7.8982 | 18000 | 0.6249 | 0.0 | 0.0 | 0.0 | 0.9075 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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