kokborok-mt

39
license:cc-by-4.0
by
MWirelabs
Other
OTHER
New
39 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by kokborok-mt with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagepythontransformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("MWirelabs/kokborok-mt")
model = AutoModelForSeq2SeqLM.from_pretrained("MWirelabs/kokborok-mt")

# English → Kokborok
tokenizer.src_lang = "eng_Latn"
tokenizer.tgt_lang = "trp_Latn"
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("trp_Latn"))
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Kokborok → English
tokenizer.src_lang = "trp_Latn"
tokenizer.tgt_lang = "eng_Latn"
inputs = tokenizer("Chwng khwbai tamo?", return_tensors="pt")
outputs = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("eng_Latn"))
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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