---
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