Instructions to use vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it") model = AutoModelForSequenceClassification.from_pretrained("vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a3d1a0ff0e5e26d9068d843591f22c6d485c622b15fbb7ff9e40934537c287d
- Size of remote file:
- 360 MB
- SHA256:
- d3805ba976a1500536fec0ef226c68129eb9f675af59661bb28e472d37366247
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