Text-Translation-Eng-To-Hindi

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AventIQ-AI
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Quick Summary

AI model with specialized capabilities.

Code Examples

🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])
🚀 Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("./quantized_model")

# Load quantized model
model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model")
model.eval()

# Run translation
translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1)

text = "How are you?"
print("Hindi:", translator(text)[0]['translation_text'])

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