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---
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
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