Instructions to use Kushrjain/custom_with_100_dim_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushrjain/custom_with_100_dim_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kushrjain/custom_with_100_dim_embedding")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kushrjain/custom_with_100_dim_embedding") model = AutoModelForSequenceClassification.from_pretrained("Kushrjain/custom_with_100_dim_embedding") - Notebooks
- Google Colab
- Kaggle
custom_with_100_dim_embedding
This model is a fine-tuned version of rohanrajpal/bert-base-codemixed-uncased-sentiment 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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