transformers pytorch tensorboard hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-wt-frz-48-Massive-intent
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: validation
      args: en-US
    metrics:
    - type: accuracy
      value: 0.8745696015740285
      name: Accuracy
---

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# hbertv1-wt-frz-48-Massive-intent

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8314
- Accuracy: 0.8746

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9303        | 1.0   | 180  | 0.8615          | 0.7659   |
| 0.7462        | 2.0   | 360  | 0.6661          | 0.8259   |
| 0.5125        | 3.0   | 540  | 0.6419          | 0.8342   |
| 0.3659        | 4.0   | 720  | 0.6058          | 0.8515   |
| 0.2742        | 5.0   | 900  | 0.6297          | 0.8539   |
| 0.1975        | 6.0   | 1080 | 0.6507          | 0.8510   |
| 0.1486        | 7.0   | 1260 | 0.6978          | 0.8500   |
| 0.1109        | 8.0   | 1440 | 0.7019          | 0.8608   |
| 0.0789        | 9.0   | 1620 | 0.7188          | 0.8598   |
| 0.0579        | 10.0  | 1800 | 0.7707          | 0.8628   |
| 0.0362        | 11.0  | 1980 | 0.7928          | 0.8647   |
| 0.0215        | 12.0  | 2160 | 0.7807          | 0.8697   |
| 0.0115        | 13.0  | 2340 | 0.8247          | 0.8701   |
| 0.0068        | 14.0  | 2520 | 0.8314          | 0.8746   |
| 0.0048        | 15.0  | 2700 | 0.8271          | 0.8731   |


### Framework versions

- Transformers 4.31.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.1
- Tokenizers 0.13.3
