File size: 1,582 Bytes
fdfd506 d181442 fdfd506 d181442 fdfd506 d181442 fdfd506 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Models-RoBERTa-1704501009.345538
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. -->
# Models-RoBERTa-1704501009.345538
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an MNLI dataset.
It achieves the following results on the evaluation set (1000 instances of MNLI validation matched):
- eval_loss: 0.2357
- eval_accuracy: 0.92
- eval_runtime: 7.4465
- eval_samples_per_second: 134.292
- eval_steps_per_second: 4.297
- epoch: 1.03
- step: 12597
## Model description
The baseline NLI model is a fine-tuned version of *roberta-base* for Text Classifacation
on the MNLI dataset , with entailments as label 0 and all others (neutral or contradiction)
as label 1.
Two classes:
* entailment: 0
* non-entailment: 1
## Intended uses & limitations
More information needed
## Training and evaluation data
Model's performance on the validation sets:
```
MNLI: 92.07%
MNLI-mm: 92.09%
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|