Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from keremp/deberta-em: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/keremp/deberta-em/resolve/main/README.md
- Command line
-
hf download hf://keremp/deberta-em/README.md
-
curl -L -o README.md https://huggingface.co/keremp/deberta-em/resolve/main/README.md
1.3 kB
metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-em
results: []
deberta-em
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0787
- F1: 0.9369
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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1