transformers safetensors bert text-classification generated_from_trainer dataset:quora base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- quora
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: qqp_v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: quora
      type: quora
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9073190036854732
      name: Accuracy
---

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# qqp_v2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the quora dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2537
- Accuracy: 0.9073

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2591        | 1.0   | 5054  | 0.2429          | 0.8948   |
| 0.186         | 2.0   | 10108 | 0.2342          | 0.9058   |
| 0.1349        | 3.0   | 15162 | 0.2537          | 0.9073   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
