Instructions to use wenkai-li/finetuned-marktextepoch-n500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wenkai-li/finetuned-marktextepoch-n500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="wenkai-li/finetuned-marktextepoch-n500")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("wenkai-li/finetuned-marktextepoch-n500") model = AutoModelForMaskedLM.from_pretrained("wenkai-li/finetuned-marktextepoch-n500") - Notebooks
- Google Colab
- Kaggle
finetuned-marktextepoch-n500
This model is a fine-tuned version of leokai/finetuned-marktextepoch-n200 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.4281
- eval_runtime: 11.4175
- eval_samples_per_second: 279.571
- eval_steps_per_second: 34.946
- epoch: 218.0
- step: 350108
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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