Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rithwik-db/finetuning-sentiment-model-3000-samples-4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") model = AutoModelForSequenceClassification.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f19eebdeafc7b0025ee82ec506f5d9a77716c2b5888c4709f97c2899e5ccc4e
- Size of remote file:
- 3.58 kB
- SHA256:
- f746026ce1d0a84d7474c047b17d9d9c86163d725207fc1bfcd7171f2ca14f70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.