Text Classification
Transformers
Safetensors
Catalan
roberta
eamt-2026
catalan
valencian
translationese
reward-model
text-embeddings-inference
Instructions to use guerreropaula/ht_mt_classifier_best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guerreropaula/ht_mt_classifier_best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="guerreropaula/ht_mt_classifier_best")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("guerreropaula/ht_mt_classifier_best") model = AutoModelForSequenceClassification.from_pretrained("guerreropaula/ht_mt_classifier_best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download data/train.csv from guerreropaula/ht_mt_classifier_best: direct link, hf CLI and curl.
- Browser
- Download file 19.2 MB
-
https://huggingface.co/guerreropaula/ht_mt_classifier_best/resolve/main/data/train.csv
- Command line
-
hf download hf://guerreropaula/ht_mt_classifier_best/data/train.csv
-
curl -L -o train.csv https://huggingface.co/guerreropaula/ht_mt_classifier_best/resolve/main/data/train.csv
19.2 MB
- Xet hash:
- 57712a8c534520f85ad164710f6a0794187d12c4e326681780d39866c770e5f9
- Size of remote file:
- 19.2 MB
- SHA256:
- a31ac1b6f523e2a71d965a481f4731e7c6a0a0ef53bd23611487b7187edbcb35
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