---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-fomc
  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. -->

# roberta-large-fomc

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7874
- Accuracy: 0.6660

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.0083 | 1    | 1.0582          | 0.4980   |
| 1.0574        | 0.2149 | 26   | 1.0428          | 0.4980   |
| 1.0702        | 0.4215 | 51   | 1.0500          | 0.4980   |
| 1.1065        | 0.6281 | 76   | 1.0374          | 0.4980   |
| 1.0241        | 0.8347 | 101  | 1.0391          | 0.4980   |
| 1.0324        | 1.0    | 121  | 1.0191          | 0.4980   |
| 1.0324        | 1.0413 | 126  | 1.0097          | 0.4980   |
| 0.9751        | 1.2479 | 151  | 1.0542          | 0.4737   |
| 1.0134        | 1.4545 | 176  | 0.9746          | 0.5931   |
| 0.9276        | 1.6612 | 201  | 0.8633          | 0.5648   |
| 0.8469        | 1.8678 | 226  | 0.7729          | 0.6538   |
| 0.7992        | 2.0    | 242  | 0.7874          | 0.6660   |
| 0.8853        | 2.0744 | 251  | 0.8597          | 0.6680   |
| 0.6466        | 2.2810 | 276  | 0.7767          | 0.6498   |
| 0.778         | 2.4876 | 301  | 1.0588          | 0.6498   |
| 0.7202        | 2.6942 | 326  | 0.7493          | 0.6721   |
| 0.7108        | 2.9008 | 351  | 0.8892          | 0.6397   |
| 0.6354        | 3.0    | 363  | 0.8265          | 0.6579   |
| 0.7704        | 3.1074 | 376  | 0.7833          | 0.6781   |
| 0.6867        | 3.3140 | 401  | 0.9702          | 0.6478   |
| 0.6973        | 3.5207 | 426  | 1.0300          | 0.6700   |
| 0.6682        | 3.7273 | 451  | 0.8206          | 0.6781   |
| 0.6605        | 3.9339 | 476  | 0.8862          | 0.6822   |
| 0.8521        | 4.0    | 484  | 0.8093          | 0.6316   |
| 0.6442        | 4.1405 | 501  | 0.9483          | 0.6437   |
| 0.577         | 4.3471 | 526  | 0.8860          | 0.6883   |
| 0.5252        | 4.5537 | 551  | 0.8797          | 0.7045   |
| 0.5274        | 4.7603 | 576  | 0.7289          | 0.7024   |
| 0.467         | 4.9669 | 601  | 0.8224          | 0.6903   |
| 0.467         | 5.0    | 605  | 0.8218          | 0.6903   |


### Framework versions

- Transformers 4.40.2
- Pytorch 1.12.0
- Datasets 2.19.1
- Tokenizers 0.19.1