---
license: mit
base_model: gpt2-xl
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train600_eval300_v1_docidx
metrics:
- accuracy
model-index:
- name: lmind_nq_train600_eval300_v1_docidx_gpt2-xl
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_nq_train600_eval300_v1_docidx
      type: tyzhu/lmind_nq_train600_eval300_v1_docidx
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8462492584802891
---

<!-- 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. -->

# lmind_nq_train600_eval300_v1_docidx_gpt2-xl

This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the tyzhu/lmind_nq_train600_eval300_v1_docidx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3697
- Accuracy: 0.8462

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4931        | 0.5   | 28   | 2.3904          | 0.5749   |
| 2.4699        | 1.0   | 56   | 2.1014          | 0.6050   |
| 1.9006        | 1.5   | 84   | 1.8079          | 0.6393   |
| 1.9317        | 2.0   | 112  | 1.5510          | 0.6722   |
| 1.3984        | 2.5   | 140  | 1.2850          | 0.7075   |
| 1.3662        | 3.0   | 168  | 1.0900          | 0.7374   |
| 0.9041        | 3.5   | 196  | 0.8909          | 0.7670   |
| 0.9056        | 4.0   | 224  | 0.7502          | 0.7903   |
| 0.6131        | 4.5   | 252  | 0.6304          | 0.8067   |
| 0.6101        | 5.0   | 280  | 0.5429          | 0.8215   |
| 0.3772        | 5.5   | 308  | 0.4872          | 0.8287   |
| 0.4265        | 6.0   | 336  | 0.4437          | 0.8357   |
| 0.2552        | 6.5   | 364  | 0.4226          | 0.8389   |
| 0.2875        | 7.0   | 392  | 0.4019          | 0.8418   |
| 0.1874        | 7.5   | 420  | 0.3965          | 0.8430   |
| 0.1958        | 8.0   | 448  | 0.3812          | 0.8441   |
| 0.1443        | 8.5   | 476  | 0.3852          | 0.8450   |
| 0.1535        | 9.0   | 504  | 0.3791          | 0.8456   |
| 0.1236        | 9.5   | 532  | 0.3849          | 0.8456   |
| 0.1221        | 10.0  | 560  | 0.3697          | 0.8462   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1