---
license: mit
tags:
- generated_from_trainer
datasets:
- conllpp
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-md-conllpp
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conllpp
      type: conllpp
      config: conllpp
      split: train
      args: conllpp
    metrics:
    - name: Precision
      type: precision
      value: 0.9971177780689113
    - name: Recall
      type: recall
      value: 0.9968043586452576
    - name: F1
      type: f1
      value: 0.9969610437242934
    - name: Accuracy
      type: accuracy
      value: 0.995003768708948
---

<!-- 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-md-conllpp

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0457
- Precision: 0.9971
- Recall: 0.9968
- F1: 0.9970
- Accuracy: 0.9950

## 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: 1e-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
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0748        | 1.0   | 878  | 0.0309          | 0.9959    | 0.9962 | 0.9961 | 0.9935   |
| 0.0111        | 2.0   | 1756 | 0.0346          | 0.9974    | 0.9967 | 0.9970 | 0.9951   |
| 0.0057        | 3.0   | 2634 | 0.0348          | 0.9974    | 0.9960 | 0.9967 | 0.9946   |
| 0.0031        | 4.0   | 3512 | 0.0434          | 0.9976    | 0.9964 | 0.9970 | 0.9951   |
| 0.0017        | 5.0   | 4390 | 0.0457          | 0.9971    | 0.9968 | 0.9970 | 0.9950   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1