Fill-Mask
Transformers
Safetensors
Persian
roberta
masked-lm
persian
farsi
ner
relation-extraction
Eval Results (legacy)
Instructions to use selfms/persian_roberta_opt_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfms/persian_roberta_opt_tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="selfms/persian_roberta_opt_tokenizer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("selfms/persian_roberta_opt_tokenizer") model = AutoModelForMaskedLM.from_pretrained("selfms/persian_roberta_opt_tokenizer") - Notebooks
- Google Colab
- Kaggle
update readme
Browse files
README.md
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A compact RoBERTa-style **Masked Language Model (MLM)** for Persian (Farsi).
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We trained a Persian BPE tokenizer on a mixed corpus combining formal text with social-media and chat data.
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The model is pre-trained with
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- **NER** on a **merged ARMAN + PEYMA** corpus
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- **Relation Extraction** on **PERLEX**
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A compact RoBERTa-style **Masked Language Model (MLM)** for Persian (Farsi).
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We trained a Persian BPE tokenizer on a mixed corpus combining formal text with social-media and chat data.
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The model is pre-trained with this tokenizer, optimized for Persian script and evaluated on two downstream tasks:
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- **NER** on a **merged ARMAN + PEYMA** corpus
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- **Relation Extraction** on **PERLEX**
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