Instructions to use vocabtrimmer/xlm-v-base-tweet-sentiment-it-trimmed-it-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocabtrimmer/xlm-v-base-tweet-sentiment-it-trimmed-it-5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vocabtrimmer/xlm-v-base-tweet-sentiment-it-trimmed-it-5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/xlm-v-base-tweet-sentiment-it-trimmed-it-5000") model = AutoModelForSequenceClassification.from_pretrained("vocabtrimmer/xlm-v-base-tweet-sentiment-it-trimmed-it-5000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52519261af318ef88b2cbb0e9fd4e5296bee3b171617c15c78af421bb2c55e7d
- Size of remote file:
- 360 MB
- SHA256:
- a0ed867b2d318eb3ec51a6a44f70af430e545fac221e8b578857132176343c6b
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