---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: l3cube-pune/hing-roberta
model-index:
- name: hing-roberta-CM-run-1
  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. -->

# hing-roberta-CM-run-1

This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4241
- Accuracy: 0.7787
- Precision: 0.7367
- Recall: 0.7378
- F1: 0.7357

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8552        | 1.0   | 497  | 0.6797          | 0.7103   | 0.6657    | 0.6872 | 0.6648 |
| 0.5998        | 2.0   | 994  | 0.6946          | 0.7304   | 0.6870    | 0.7108 | 0.6933 |
| 0.4146        | 3.0   | 1491 | 0.9422          | 0.7465   | 0.7215    | 0.6734 | 0.6887 |
| 0.2592        | 4.0   | 1988 | 1.3122          | 0.7626   | 0.7240    | 0.7130 | 0.7126 |
| 0.1644        | 5.0   | 2485 | 1.7526          | 0.7344   | 0.6856    | 0.6901 | 0.6875 |
| 0.1022        | 6.0   | 2982 | 1.9479          | 0.7746   | 0.7331    | 0.7317 | 0.7316 |
| 0.0764        | 7.0   | 3479 | 2.0772          | 0.7626   | 0.7190    | 0.7214 | 0.7202 |
| 0.0468        | 8.0   | 3976 | 2.2799          | 0.7626   | 0.7184    | 0.7044 | 0.7099 |
| 0.0472        | 9.0   | 4473 | 2.2257          | 0.7586   | 0.7103    | 0.7176 | 0.7135 |
| 0.0306        | 10.0  | 4970 | 2.3307          | 0.7505   | 0.7068    | 0.7081 | 0.7074 |
| 0.0351        | 11.0  | 5467 | 2.2555          | 0.7666   | 0.7198    | 0.7254 | 0.7219 |
| 0.0328        | 12.0  | 5964 | 2.4425          | 0.7626   | 0.7258    | 0.7124 | 0.7179 |
| 0.0225        | 13.0  | 6461 | 2.5229          | 0.7666   | 0.7237    | 0.7138 | 0.7179 |
| 0.0232        | 14.0  | 6958 | 2.5717          | 0.7646   | 0.7202    | 0.7115 | 0.7144 |
| 0.0191        | 15.0  | 7455 | 2.4027          | 0.7606   | 0.7110    | 0.7202 | 0.7152 |
| 0.0175        | 16.0  | 7952 | 2.3918          | 0.7666   | 0.7216    | 0.7241 | 0.7226 |
| 0.0087        | 17.0  | 8449 | 2.4176          | 0.7767   | 0.7347    | 0.7365 | 0.7345 |
| 0.0077        | 18.0  | 8946 | 2.4231          | 0.7686   | 0.7201    | 0.7265 | 0.7230 |
| 0.0095        | 19.0  | 9443 | 2.4162          | 0.7827   | 0.7392    | 0.7436 | 0.7406 |
| 0.0063        | 20.0  | 9940 | 2.4241          | 0.7787   | 0.7367    | 0.7378 | 0.7357 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1