---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-cased-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: en_ewt
      split: validation
      args: en_ewt
    metrics:
    - name: Precision
      type: precision
      value: 0.8688031595250056
    - name: Recall
      type: recall
      value: 0.8557292884764056
    - name: F1
      type: f1
      value: 0.8617720995316154
    - name: Accuracy
      type: accuracy
      value: 0.8904395106479384
---

<!-- 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. -->

# bert-large-cased-upos

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4114
- Precision: 0.8688
- Recall: 0.8557
- F1: 0.8618
- Accuracy: 0.8904

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 438  | 0.5774          | 0.8348    | 0.7595 | 0.7893 | 0.8099   |
| No log        | 2.0   | 876  | 0.4787          | 0.8114    | 0.7967 | 0.8000 | 0.8385   |
| No log        | 3.0   | 1314 | 0.4345          | 0.8227    | 0.8302 | 0.8213 | 0.8601   |
| No log        | 4.0   | 1752 | 0.4140          | 0.8257    | 0.8430 | 0.8304 | 0.8727   |
| No log        | 5.0   | 2190 | 0.4211          | 0.8405    | 0.8525 | 0.8441 | 0.8787   |
| No log        | 6.0   | 2628 | 0.4114          | 0.8688    | 0.8557 | 0.8618 | 0.8904   |
| No log        | 7.0   | 3066 | 0.4582          | 0.8454    | 0.8572 | 0.8503 | 0.8911   |
| No log        | 8.0   | 3504 | 0.4771          | 0.8447    | 0.8588 | 0.8508 | 0.8894   |
| No log        | 9.0   | 3942 | 0.4799          | 0.8545    | 0.8626 | 0.8577 | 0.8918   |
| No log        | 10.0  | 4380 | 0.4919          | 0.8539    | 0.8642 | 0.8579 | 0.8937   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1