opus-mt-en-roa_multilanguageTranslation

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Quick Summary

AI model with specialized capabilities.

Code Examples

Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Model Detailstext
Base Model: Helsinki-NLP/opus-mt-en-roa

Training Dataset: Tatoeba dataset

Fine-tuned Language Pairs: en-mr, eo-nl, es-pt, fr-ru, es-gl

Evaluation Metric: BLEU Score (using sacreBLEU)

Training Framework: Hugging Face Transformers

Training Configuration

Optimizer: AdamW

Learning Rate: 2e-5

Batch Size: 16 (per device)

Weight Decay: 0.01

Epochs: 3

Precision: FP32 (initial training), converted to FP16 for inference
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Inference Exampletexttransformers
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model = AutoModelForSeq2SeqLM.from_pretrained("fine_tuned_models_fp16/en-mr/final/", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("fine_tuned_models_fp16/en-mr/final/")

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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