Instructions to use disanda/first_try_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disanda/first_try_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="disanda/first_try_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("disanda/first_try_4") model = AutoModelForMaskedLM.from_pretrained("disanda/first_try_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from disanda/first_try_4: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/disanda/first_try_4/resolve/main/README.md
- Command line
-
hf download hf://disanda/first_try_4/README.md
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curl -L -o README.md https://huggingface.co/disanda/first_try_4/resolve/main/README.md
1.31 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: first_try_4
results: []
first_try_4
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 2.5505
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.7226 | 1.0 | 157 | 2.5273 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.12.0+cu102
- Datasets 2.12.0
- Tokenizers 0.13.3