---
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-concat-2
  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. -->

# bert-concat-2

This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7060

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6866        | 0.52  | 1000  | 6.2709          |
| 6.2315        | 1.04  | 2000  | 6.2177          |
| 6.1818        | 1.56  | 3000  | 6.1895          |
| 6.1511        | 2.08  | 4000  | 6.1559          |
| 6.0984        | 2.6   | 5000  | 6.1185          |
| 6.0611        | 3.12  | 6000  | 6.0668          |
| 6.0114        | 3.65  | 7000  | 6.0361          |
| 5.9679        | 4.17  | 8000  | 6.0160          |
| 5.9272        | 4.69  | 9000  | 5.9731          |
| 5.8904        | 5.21  | 10000 | 5.9424          |
| 5.8557        | 5.73  | 11000 | 5.9190          |
| 5.8237        | 6.25  | 12000 | 5.9002          |
| 5.8008        | 6.77  | 13000 | 5.8787          |
| 5.7785        | 7.29  | 14000 | 5.8644          |
| 5.7569        | 7.81  | 15000 | 5.8534          |
| 5.7305        | 8.33  | 16000 | 5.8429          |
| 5.7187        | 8.85  | 17000 | 5.8283          |
| 5.699         | 9.38  | 18000 | 5.8124          |
| 5.6737        | 9.9   | 19000 | 5.8055          |
| 5.648         | 10.42 | 20000 | 5.7945          |
| 5.641         | 10.94 | 21000 | 5.7869          |
| 5.613         | 11.46 | 22000 | 5.7700          |
| 5.6078        | 11.98 | 23000 | 5.7659          |
| 5.5759        | 12.5  | 24000 | 5.7555          |
| 5.5682        | 13.02 | 25000 | 5.7522          |
| 5.5461        | 13.54 | 26000 | 5.7397          |
| 5.5414        | 14.06 | 27000 | 5.7349          |
| 5.5195        | 14.58 | 28000 | 5.7310          |
| 5.5081        | 15.1  | 29000 | 5.7214          |
| 5.4922        | 15.62 | 30000 | 5.7188          |
| 5.4858        | 16.15 | 31000 | 5.7127          |
| 5.4786        | 16.67 | 32000 | 5.7092          |
| 5.4685        | 17.19 | 33000 | 5.7075          |
| 5.4571        | 17.71 | 34000 | 5.7060          |
| 5.4592        | 18.23 | 35000 | 5.7018          |
| 5.4555        | 18.75 | 36000 | 5.7043          |
| 5.4512        | 19.27 | 37000 | 5.7028          |
| 5.4522        | 19.79 | 38000 | 5.7060          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3