nllb-200-distilled-600M-en-to-arz

13
2 languages
license:cc-by-nc-4.0
by
IbrahimAmin
Language Model
OTHER
New
13 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 nllb-200-distilled-600M-en-to-arz 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
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'
🚀 Usagepythontransformers
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", torch_dtype=torch.float16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/nllb-200-distilled-600M-en-to-arz", src_lang="eng_Latn", tgt_lang="arz_Arab")

article = "How are you doing today?"
inputs = tokenizer(article, return_tensors="pt").to(device)

translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("arz_Arab"))
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

# Output: 'إزيك النهاردة؟'

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