Downloads · 30 days
14
22% of all-time downloads
roberta-large-mnli-model1
varun-v-rao/roberta-large-mnli-model1
roberta-large-mnli-model1 is a text classification model from varun-v-rao. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
63
Public
Parameters
355M
4.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.4 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jan 24, 2024
- Updated
- Jan 24, 2024
- SHA
- 8550012c
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
roberta-large-mnli-model1
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3543
- Accuracy: 0.9029
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: 64
- eval_batch_size: 64
- seed: 27
- 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 |
|---|---|---|---|---|
| 0.3086 | 1.0 | 6136 | 0.2711 | 0.9009 |
| 0.2122 | 2.0 | 12272 | 0.2934 | 0.9056 |
| 0.1323 | 3.0 | 18408 | 0.3543 | 0.9029 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jan 24, 2024
- Updated
- Jan 24, 2024