transformers pytorch tensorboard hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-tiny-wt-48-Massive-intent-emb-comp
  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: 0.7899655681259223
      name: Accuracy
---

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# hbertv1-tiny-wt-48-Massive-intent-emb-comp

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8545
- Accuracy: 0.7900

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6847        | 1.0   | 180  | 3.2207          | 0.2710   |
| 2.7795        | 2.0   | 360  | 2.3154          | 0.4471   |
| 2.0459        | 3.0   | 540  | 1.7680          | 0.5627   |
| 1.5874        | 4.0   | 720  | 1.4363          | 0.6734   |
| 1.2902        | 5.0   | 900  | 1.2306          | 0.7127   |
| 1.0905        | 6.0   | 1080 | 1.1068          | 0.7373   |
| 0.9468        | 7.0   | 1260 | 1.0113          | 0.7545   |
| 0.844         | 8.0   | 1440 | 0.9661          | 0.7580   |
| 0.7684        | 9.0   | 1620 | 0.9333          | 0.7649   |
| 0.7086        | 10.0  | 1800 | 0.9018          | 0.7772   |
| 0.6629        | 11.0  | 1980 | 0.8807          | 0.7831   |
| 0.6244        | 12.0  | 2160 | 0.8747          | 0.7796   |
| 0.5965        | 13.0  | 2340 | 0.8591          | 0.7875   |
| 0.5731        | 14.0  | 2520 | 0.8634          | 0.7875   |
| 0.5633        | 15.0  | 2700 | 0.8545          | 0.7900   |


### Framework versions

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