---
base_model: roberta-large
license: mit
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: robertaL_ner
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/adam-fendri/huggingface/runs/9naabn8w)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/adam-fendri/huggingface/runs/9naabn8w)
# robertaL_ner

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: 0.2206
- Accuracy: 0.9558
- F1: 0.9558
- Precision: 0.9560
- Recall: 0.9558

## 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: 3e-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: 200
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.331         | 1.0   | 49   | 1.0308          | 0.6698   | 0.6131 | 0.6094    | 0.6698 |
| 0.8659        | 2.0   | 98   | 0.6391          | 0.7992   | 0.7929 | 0.7932    | 0.7992 |
| 0.5051        | 3.0   | 147  | 0.5164          | 0.8446   | 0.8389 | 0.8410    | 0.8446 |
| 0.4183        | 4.0   | 196  | 0.3752          | 0.8840   | 0.8827 | 0.8824    | 0.8840 |
| 0.4014        | 5.0   | 245  | 0.3487          | 0.8946   | 0.8926 | 0.8921    | 0.8946 |
| 0.2955        | 6.0   | 294  | 0.3009          | 0.9040   | 0.9049 | 0.9068    | 0.9040 |
| 0.2525        | 7.0   | 343  | 0.2478          | 0.9303   | 0.9303 | 0.9304    | 0.9303 |
| 0.2381        | 8.0   | 392  | 0.2498          | 0.9240   | 0.9243 | 0.9248    | 0.9240 |
| 0.2255        | 9.0   | 441  | 0.2214          | 0.9321   | 0.9318 | 0.9323    | 0.9321 |
| 0.1463        | 10.0  | 490  | 0.2258          | 0.9397   | 0.9396 | 0.9396    | 0.9397 |
| 0.151         | 11.0  | 539  | 0.2271          | 0.9421   | 0.9421 | 0.9422    | 0.9421 |
| 0.1213        | 12.0  | 588  | 0.2146          | 0.9500   | 0.9498 | 0.9499    | 0.9500 |
| 0.1166        | 13.0  | 637  | 0.2162          | 0.9494   | 0.9493 | 0.9496    | 0.9494 |
| 0.121         | 14.0  | 686  | 0.2442          | 0.9421   | 0.9424 | 0.9428    | 0.9421 |
| 0.0841        | 15.0  | 735  | 0.2206          | 0.9558   | 0.9558 | 0.9560    | 0.9558 |
| 0.0485        | 16.0  | 784  | 0.2555          | 0.9452   | 0.9452 | 0.9454    | 0.9452 |
| 0.0598        | 17.0  | 833  | 0.2338          | 0.9558   | 0.9558 | 0.9559    | 0.9558 |
| 0.0462        | 18.0  | 882  | 0.2443          | 0.9549   | 0.9549 | 0.9550    | 0.9549 |
| 0.0323        | 19.0  | 931  | 0.2531          | 0.9540   | 0.9540 | 0.9542    | 0.9540 |
| 0.0466        | 20.0  | 980  | 0.2509          | 0.9549   | 0.9549 | 0.9550    | 0.9549 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1