Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use varun-v-rao/roberta-base-mnli-model1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varun-v-rao/roberta-base-mnli-model1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="varun-v-rao/roberta-base-mnli-model1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("varun-v-rao/roberta-base-mnli-model1") model = AutoModelForSequenceClassification.from_pretrained("varun-v-rao/roberta-base-mnli-model1") - Notebooks
- Google Colab
- Kaggle
roberta-base-mnli-model1
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3473
- Accuracy: 0.8743
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: 256
- eval_batch_size: 256
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4049 | 1.0 | 1534 | 0.3517 | 0.8679 |
| 0.3365 | 2.0 | 3068 | 0.3372 | 0.8714 |
| 0.2863 | 3.0 | 4602 | 0.3473 | 0.8743 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for varun-v-rao/roberta-base-mnli-model1
Base model
FacebookAI/roberta-base