Text Classification
Transformers
TensorBoard
Core ML
Safetensors
distilbert
text-embeddings-inference
Instructions to use ivanscorral/tweets-sentiment-model-60k-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivanscorral/tweets-sentiment-model-60k-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ivanscorral/tweets-sentiment-model-60k-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ivanscorral/tweets-sentiment-model-60k-samples") model = AutoModelForSequenceClassification.from_pretrained("ivanscorral/tweets-sentiment-model-60k-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ivanscorral/tweets-sentiment-model-60k-samples: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/ivanscorral/tweets-sentiment-model-60k-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://ivanscorral/tweets-sentiment-model-60k-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ivanscorral/tweets-sentiment-model-60k-samples/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 5a3b6d9e49e456b7abaac021b7e766b75833e404401415edf713cef933b9d8ef
- Size of remote file:
- 4.6 kB
- SHA256:
- db30db7f9e71bc6b2cdd49a70f693e8fff56fe4d5b5e19858ae76a502d0ed293
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