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This repository hosts a Seq2Seq model trained for sequence-to-sequence tasks, such as machine translation, summarization, or text generation. The model is fine-tuned on custom datasets to demonstrate high-quality predictions, measured using metrics like BLEU and CHRF.

**Model Details**
Model Type: Sequence-to-Sequence (Seq2Seq)
**Architecture**:
Encoder: Processes input sequences and encodes contextual information.
Decoder: Generates output sequences based on the encoded information.
Dataset: Trained on a bilingual dataset with paired sequences for translation tasks.
**Features**
Supports tasks such as:
Machine translation (e.g., English to Belarusian or Belarusian to English).
Summarization of long texts.
Text-to-text generation tasks.
Fine-tuned to achieve high CHRF scores, indicating strong alignment with target sequences.
Performance
**Evaluation Metrics:**
CHRF Score: Demonstrates consistent performance across epochs, with scores stabilizing after early improvements.
BLEU Score: Provides additional validation for translation quality.
The Seq2Seq model outperforms traditional LSTM-based models, particularly in maintaining contextual relevance during generation.
How to Use
**Model Inference:**

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "your-huggingface-username/seq2seq-model"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_text = "Enter your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Fine-tuning Instructions:

**Prepare paired datasets in the required format.**
Use the transformers library for further fine-tuning on custom tasks.
**Model Applications**
Translation: Handles text translation tasks between predefined languages.
Summarization: Provides concise summaries of input documents.
Text Generation: Generates meaningful text based on input prompts.
**Evaluation**
Trained and validated using CHRF and BLEU metrics.
Consistently achieves higher scores than baseline models, ensuring high-quality outputs.