Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use devtanumisra/finetuning-profane-model-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devtanumisra/finetuning-profane-model-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devtanumisra/finetuning-profane-model-deberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devtanumisra/finetuning-profane-model-deberta") model = AutoModelForSequenceClassification.from_pretrained("devtanumisra/finetuning-profane-model-deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
input sequence
#1
by naif576 - opened
hello there and thanks for the great work.
I wanted to ask what was the input sequence to the tokenizer.
How did you manage to combine the sentence with the aspect?
I tried to use them as a pair of sentences
for example:
text = "my name is Naif and I love math but I hate science."
aspect = "math"
tokenizer(text, aspect)
is this the correct way to do so?