---
license: apache-2.0
base_model: om-ashish-soni/pos-morph-analysis-eng
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos-morph-analysis-eng
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: en_lines
      split: validation
      args: en_lines
    metrics:
    - name: Precision
      type: precision
      value: 0.9547287488574655
    - name: Recall
      type: recall
      value: 0.9594229522368706
    - name: F1
      type: f1
      value: 0.957070094591317
    - name: Accuracy
      type: accuracy
      value: 0.9573510302580286
---

<!-- 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. -->

# pos-morph-analysis-eng

This model is a fine-tuned version of [om-ashish-soni/pos-morph-analysis-eng](https://huggingface.co/om-ashish-soni/pos-morph-analysis-eng) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2354
- Precision: 0.9547
- Recall: 0.9594
- F1: 0.9571
- Accuracy: 0.9574

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 99   | 0.2425          | 0.9476    | 0.9523 | 0.9499 | 0.9505   |
| No log        | 1.99  | 198  | 0.2253          | 0.9504    | 0.9553 | 0.9528 | 0.9540   |
| No log        | 2.99  | 297  | 0.2273          | 0.9511    | 0.9565 | 0.9538 | 0.9548   |
| No log        | 4.0   | 397  | 0.2348          | 0.9512    | 0.9559 | 0.9536 | 0.9541   |
| No log        | 5.0   | 496  | 0.2294          | 0.9539    | 0.9586 | 0.9562 | 0.9574   |
| 0.0728        | 5.99  | 595  | 0.2319          | 0.9547    | 0.9594 | 0.9570 | 0.9574   |
| 0.0728        | 6.99  | 694  | 0.2405          | 0.9540    | 0.9585 | 0.9562 | 0.9566   |
| 0.0728        | 7.98  | 792  | 0.2354          | 0.9547    | 0.9594 | 0.9571 | 0.9574   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3