Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use meghanadh/finetune_bert_iemocap_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meghanadh/finetune_bert_iemocap_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="meghanadh/finetune_bert_iemocap_text")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("meghanadh/finetune_bert_iemocap_text") model = AutoModelForSequenceClassification.from_pretrained("meghanadh/finetune_bert_iemocap_text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finetune_bert_iemocap_text
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4672
- eval_accuracy: 0.8682
- eval_f1: 0.8691
- eval_runtime: 9.9975
- eval_samples_per_second: 55.414
- eval_steps_per_second: 7.002
- step: 0
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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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