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---
license: mit
base_model: gpt2-xl
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train1000_eval200_v1_recite_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train1000_eval200_v1_recite_qa_gpt2-xl
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_hotpot_train1000_eval200_v1_recite_qa
type: tyzhu/lmind_hotpot_train1000_eval200_v1_recite_qa
metrics:
- name: Accuracy
type: accuracy
value: 0.6988971684053651
---
<!-- 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_hotpot_train1000_eval200_v1_recite_qa_gpt2-xl
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the tyzhu/lmind_hotpot_train1000_eval200_v1_recite_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4436
- Accuracy: 0.6989
## 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: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7644 | 1.0 | 211 | 1.0036 | 0.6429 |
| 0.526 | 2.0 | 422 | 0.7351 | 0.6658 |
| 0.4163 | 3.0 | 633 | 0.5744 | 0.6815 |
| 0.2864 | 4.0 | 844 | 0.4953 | 0.6899 |
| 0.2118 | 5.0 | 1055 | 0.4594 | 0.6944 |
| 0.17 | 6.0 | 1266 | 0.4490 | 0.6964 |
| 0.134 | 7.0 | 1477 | 0.4369 | 0.6979 |
| 0.1206 | 8.0 | 1688 | 0.4372 | 0.6987 |
| 0.1081 | 9.0 | 1899 | 0.4423 | 0.6987 |
| 0.1053 | 10.0 | 2110 | 0.4436 | 0.6989 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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