---
base_model: prosusai/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finbert-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. -->

# finbert-fomc

This model is a fine-tuned version of [prosusai/finbert](https://huggingface.co/prosusai/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8286
- Accuracy: 0.6154

## 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    | 1.7351          | 0.4049   |
| 1.7127        | 0.2149 | 26   | 1.4334          | 0.4777   |
| 1.3539        | 0.4215 | 51   | 1.0763          | 0.5547   |
| 1.0501        | 0.6281 | 76   | 0.9471          | 0.5547   |
| 0.9614        | 0.8347 | 101  | 0.9471          | 0.5628   |
| 0.988         | 1.0    | 121  | 0.9030          | 0.5709   |
| 0.988         | 1.0413 | 126  | 0.8999          | 0.5547   |
| 0.8862        | 1.2479 | 151  | 0.9215          | 0.5628   |
| 0.8506        | 1.4545 | 176  | 0.8740          | 0.5506   |
| 0.8825        | 1.6612 | 201  | 0.8604          | 0.6235   |
| 0.8366        | 1.8678 | 226  | 0.8063          | 0.6194   |
| 0.7666        | 2.0    | 242  | 0.8286          | 0.6154   |
| 0.7039        | 2.0744 | 251  | 0.8652          | 0.6073   |
| 0.5698        | 2.2810 | 276  | 1.0371          | 0.5911   |
| 0.7683        | 2.4876 | 301  | 0.9662          | 0.5951   |
| 0.528         | 2.6942 | 326  | 0.8984          | 0.6275   |
| 0.6282        | 2.9008 | 351  | 0.9095          | 0.6154   |
| 0.5638        | 3.0    | 363  | 0.8730          | 0.6761   |
| 0.5208        | 3.1074 | 376  | 0.9042          | 0.6599   |
| 0.4426        | 3.3140 | 401  | 1.0531          | 0.6235   |
| 0.5795        | 3.5207 | 426  | 1.0305          | 0.6559   |
| 0.363         | 3.7273 | 451  | 0.9831          | 0.6559   |
| 0.4584        | 3.9339 | 476  | 0.9950          | 0.6437   |
| 0.3794        | 4.0    | 484  | 1.0740          | 0.6154   |
| 0.2579        | 4.1405 | 501  | 1.1667          | 0.6356   |
| 0.1803        | 4.3471 | 526  | 1.0366          | 0.6883   |
| 0.1824        | 4.5537 | 551  | 1.2625          | 0.6599   |
| 0.2647        | 4.7603 | 576  | 1.2183          | 0.6761   |
| 0.205         | 4.9669 | 601  | 1.1944          | 0.6721   |
| 0.205         | 5.0    | 605  | 1.1959          | 0.6721   |


### Framework versions

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