Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
sentiment analysis
cyberbullying detection
PyTorch
Trainer
DistilBERT
text-embeddings-inference
Instructions to use LalasaMynalli/LalasaMynalli_First_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LalasaMynalli/LalasaMynalli_First_LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LalasaMynalli/LalasaMynalli_First_LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LalasaMynalli/LalasaMynalli_First_LLM") model = AutoModelForSequenceClassification.from_pretrained("LalasaMynalli/LalasaMynalli_First_LLM") - Notebooks
- Google Colab
- Kaggle
Training_Checkpoint
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0006
- eval_accuracy: 1.0
- eval_f1: 1.0
- eval_runtime: 1.7344
- eval_samples_per_second: 576.552
- eval_steps_per_second: 36.323
- epoch: 10.0
- step: 630
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for LalasaMynalli/LalasaMynalli_First_LLM
Base model
distilbert/distilbert-base-uncased