Instructions to use ZZZZCCCC/codebert_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZZZZCCCC/codebert_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ZZZZCCCC/codebert_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ZZZZCCCC/codebert_5") model = AutoModelForMaskedLM.from_pretrained("ZZZZCCCC/codebert_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from ZZZZCCCC/codebert_5: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
-
https://huggingface.co/ZZZZCCCC/codebert_5/resolve/main/README.md
- Command line
-
hf download hf://ZZZZCCCC/codebert_5/README.md
-
curl -L -o README.md https://huggingface.co/ZZZZCCCC/codebert_5/resolve/main/README.md
1.51 kB
metadata
base_model: microsoft/codebert-base-mlm
tags:
- generated_from_trainer
model-index:
- name: codebert_5
results: []
codebert_5
This model is a fine-tuned version of microsoft/codebert-base-mlm on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5732
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: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.782 | 1.0 | 524 | 0.6491 |
| 0.7042 | 2.0 | 1048 | 0.6141 |
| 0.6636 | 3.0 | 1572 | 0.5842 |
| 0.6579 | 4.0 | 2096 | 0.5818 |
| 0.6318 | 5.0 | 2620 | 0.5732 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.15.2