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
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Check out the documentation for more information.
vocabtrimmer/xlm-v-base-trimmed-it-5000-tweet-sentiment-it
This model is a fine-tuned version of /home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-it-5000 on the
cardiffnlp/tweet_sentiment_multilingual (italian).
Following metrics are computed on the test split of
cardiffnlp/tweet_sentiment_multilingual(italian).
| eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy | |
|---|---|---|---|---|---|---|---|
| 0 | 67.36 | 67.36 | 67.36 | 67.11 | 67.36 | 67.93 | 67.36 |
Check the result file here.
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