Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use VinsmokeMir/Further_fine_tuning_E9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VinsmokeMir/Further_fine_tuning_E9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VinsmokeMir/Further_fine_tuning_E9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VinsmokeMir/Further_fine_tuning_E9") model = AutoModelForSequenceClassification.from_pretrained("VinsmokeMir/Further_fine_tuning_E9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Further_fine_tuning_E9
This model is a fine-tuned version of rafsankabir/Pretrained_E10 on the xnli_bn 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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