# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="QomSSLab/etghan_tagging_v1")# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("QomSSLab/etghan_tagging_v1")
model = AutoModelForTokenClassification.from_pretrained("QomSSLab/etghan_tagging_v1")Quick Links
QomSSLab/etghan_tagging_v1
This repository hosts an XLM-RoBERTa token-classification head trained.
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model_id = "QomSSLab/etghan_tagging_v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)
tagger = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
text = "مثال از یک ورودی فارسی"
for entity in tagger(text):
print(entity)
Labels
AABSENCEAPPEALABILITYAPPEAL_AUTHORITYAPPEAL_DEADLINEBBSEMHECOURT_NAMEDEFENDANT_ADDRESSDEFENDANT_FATHERDEFENDANT_NAMEFINALITYJUDGE_NAMEJUDGE_POSITIONJUDGE_SIGNATUREJUDGMENT_DATELEGAL_CHARGELEGAL_REPRESENTATIVEOPLACE_OF_OFFENSEPLACE_OF_OFFICEPLAINTIFF_ADDRESSPLAINTIFF_CLAIMPLAINTIFF_FATHERPLAINTIFF_NAMEPRESENCESUBJECT_OF_CLAIMTIME_OF_OFFENSE
Training
- Base model:
xlm-roberta-large - Optimizer/args: default Trainer settings (AdamW, lr=3e-5, batch size 8, epochs configurable)
- Downloads last month
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# Gated model: Login with a HF token with gated access permission hf auth login