Instructions to use tachiwin/language_classification_curricular_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tachiwin/language_classification_curricular_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="tachiwin/language_classification_curricular_checkpoints")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("tachiwin/language_classification_curricular_checkpoints") model = AutoModelForSpeechSeq2Seq.from_pretrained("tachiwin/language_classification_curricular_checkpoints") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use tachiwin/language_classification_curricular_checkpoints with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tachiwin/language_classification_curricular_checkpoints to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tachiwin/language_classification_curricular_checkpoints to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tachiwin/language_classification_curricular_checkpoints to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="tachiwin/language_classification_curricular_checkpoints", max_seq_length=2048, )
language_classification_curricular_checkpoints
This model is a fine-tuned version of unsloth/whisper-large-v3-turbo on an unknown dataset.
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: 0.0002
- train_batch_size: 6
- eval_batch_size: 1
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 69
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.56.2
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.2
- Downloads last month
- 9
Model tree for tachiwin/language_classification_curricular_checkpoints
Base model
openai/whisper-large-v3 Finetuned
unsloth/whisper-large-v3-turbo