---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-large-PM-M3-Voc-hf-finetuned-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. -->

# RoBERTa-large-PM-M3-Voc-hf-finetuned-ner

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2320
- Precision: 0.7794
- Recall: 0.9175
- F1: 0.8429
- Accuracy: 0.9470

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 36   | 1.0441          | 0.2844    | 0.0828 | 0.1283 | 0.7449   |
| No log        | 2.0   | 72   | 0.7648          | 0.3878    | 0.5899 | 0.4680 | 0.7606   |
| No log        | 3.0   | 108  | 0.5539          | 0.5096    | 0.6022 | 0.5520 | 0.8166   |
| No log        | 4.0   | 144  | 0.6093          | 0.4714    | 0.7188 | 0.5694 | 0.7981   |
| No log        | 5.0   | 180  | 0.5084          | 0.5185    | 0.7530 | 0.6141 | 0.8381   |
| No log        | 6.0   | 216  | 0.3945          | 0.6099    | 0.7329 | 0.6658 | 0.8775   |
| No log        | 7.0   | 252  | 0.4960          | 0.5273    | 0.7961 | 0.6344 | 0.8458   |
| No log        | 8.0   | 288  | 0.3364          | 0.6501    | 0.8013 | 0.7178 | 0.9002   |
| No log        | 9.0   | 324  | 0.3166          | 0.6601    | 0.8418 | 0.7399 | 0.9092   |
| No log        | 10.0  | 360  | 0.2691          | 0.7087    | 0.8470 | 0.7717 | 0.9233   |
| No log        | 11.0  | 396  | 0.2663          | 0.7215    | 0.8652 | 0.7868 | 0.9290   |
| No log        | 12.0  | 432  | 0.2877          | 0.7138    | 0.8904 | 0.7924 | 0.9238   |
| No log        | 13.0  | 468  | 0.2712          | 0.7353    | 0.8990 | 0.8090 | 0.9321   |
| 0.4329        | 14.0  | 504  | 0.2485          | 0.7511    | 0.8990 | 0.8184 | 0.9387   |
| 0.4329        | 15.0  | 540  | 0.2236          | 0.7859    | 0.9056 | 0.8416 | 0.9474   |
| 0.4329        | 16.0  | 576  | 0.2392          | 0.7696    | 0.9131 | 0.8352 | 0.9439   |
| 0.4329        | 17.0  | 612  | 0.2420          | 0.7684    | 0.9157 | 0.8356 | 0.9438   |
| 0.4329        | 18.0  | 648  | 0.2375          | 0.7708    | 0.9172 | 0.8377 | 0.9445   |
| 0.4329        | 19.0  | 684  | 0.2299          | 0.7832    | 0.9179 | 0.8452 | 0.9478   |
| 0.4329        | 20.0  | 720  | 0.2320          | 0.7794    | 0.9175 | 0.8429 | 0.9470   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1