T5 Model Architecture Breakdown
describe me this model accurately . each step precisely and
accurately and easy to learn
The image represents the T5 Transformer Model Architecture. Here's an accurate, step-bystep explanation of each component and the flow in the model:
1. Input Representation (Bottom Left)
The inputs, labeled as x₁ (Thinking) and x₂ (Machines), are tokenized and mapped to
vectors.
Positional Encoding is added to each input to retain the position of tokens in the
sequence, as Transformers do not have recurrence or convolution.
2. Encoder Stack (Left Side in Green)
The model processes inputs through multiple Encoders stacked sequentially.
Encoder #1 (First Block):
Self-Attention Layer:
Input tokens are passed into a Self-Attention layer where each token attends to
every other token to capture relationships between words.
This helps to understand contextual meaning within the input sentence.
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Add & Normalize:
Outputs of the Self-Attention layer are added to the input using a residual
connection.
The combined result is then passed through Layer Normalization to stabilize
training.
Feed Forward Network:
The normalized output is passed through a Feed Forward Network (a fully
connected neural network applied to each position independently).
This layer improves non-linearity.
Add & Normalize (again):
A second residual connection adds the Feed Forward output to the previous layer's
input, followed by normalization.
Encoder #2 (Second Block):
The same process repeats as in Encoder #1:
Self-Attention → Add & Normalize → Feed Forward → Add & Normalize.
The output from Encoder #1 is fed into Encoder #2, creating a deeper representation of
the input.
3. Decoder Stack (Right Side in Pink)
The Decoder processes encoded input features to generate output tokens.
Decoder #1 (First Decoder Block):
Masked Self-Attention:
The input to the decoder (e.g., previously generated tokens) goes through a Masked
Self-Attention layer.
Masking ensures that predictions for a given position do not depend on future
positions (ensuring causal behavior).
Add & Normalize:
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Outputs of the Self-Attention layer are added to the input using a residual
connection, followed by normalization.
Encoder-Decoder Attention:
The output from the Encoder Stack is fed into an Encoder-Decoder Attention layer.
This enables the decoder to attend to relevant parts of the encoder's output while
generating tokens.
Add & Normalize (again):
The result of Encoder-Decoder Attention is combined with inputs through another
residual connection and normalization.
Feed Forward Network:
The normalized output is passed through a fully connected Feed Forward Network
to introduce non-linearity.
Add & Normalize (final step):
Residual connections and normalization finalize the block.
Decoder #2 (Second Decoder Block):
Similar steps occur in Decoder #2 as in Decoder #1:
Masked Self-Attention → Add & Normalize → Encoder-Decoder Attention → Add
& Normalize → Feed Forward → Add & Normalize.
This ensures a deeper processing of decoder features and alignment with encoder
outputs.
4. Output Layer (Top Right)
Linear Layer:
The final output from the Decoder Stack is passed through a Linear Layer to map
high-dimensional features into logits for each token in the vocabulary.
Softmax Layer:
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The logits are passed through a Softmax function to produce probabilities over the
vocabulary.
The token with the highest probability is selected as the next output token.
Key Summary
1. Input: Input tokens with positional encodings are processed through stacked Encoders.
2. Encoder: Each Encoder processes input tokens using Self-Attention and Feed Forward
Networks with residual connections.
3. Decoder: The Decoder generates output tokens sequentially using Masked SelfAttention, Encoder-Decoder Attention, and Feed Forward Networks.
4. Output: The final output is passed through Linear and Softmax layers to predict the
next token.
This process continues until the model generates the entire sequence of output tokens.
Let me know if you need further clarification or simplifications!
do same for this methodology
This image represents a Proposed Methodology for translating Bangla regional dialects into
Simple Bangla using a step-by-step process. Below is a clear breakdown of each component:
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1. Dataset
The input dataset consists of Bangla Regional Dialects from the following regions:
Barishal
Noakhali
Mymensingh
Sylhet
Chittagong
This serves as the raw data for translation.
2. Preprocessing
The raw dataset undergoes Preprocessing to prepare clean input data for the model:
Normalization:
Remove Digits
Remove Web-links
Remove Punctuation
Remove Symbols
Remove non-Bengali letters
Remove Stop Words
Tokenization:
Splitting text into smaller units like words or subwords.
Lemmatization:
Reducing words to their root or base forms.
Purpose: To ensure the data is clean, consistent, and ready for modeling.
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3. Model
Two models are employed for the translation task:
mT5: A multilingual version of the T5 transformer model.
BanglaT5: A variant of T5 fine-tuned specifically for Bangla translation tasks.
4. Translation
The preprocessed data is fed into the mT5 and BanglaT5 models.
The models translate the input text (Bangla regional dialect) into Simple Bangla.
5. Evaluation
The translated output is evaluated using multiple metrics to measure the model's
performance:
Character Error Rate (CER)
Word Error Rate (WER)
BLEU (Bilingual Evaluation Understudy)
METEOR (Metric for Evaluation of Translation with Explicit ORdering)
ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
Accuracy
Precision
Recall
These metrics ensure comprehensive evaluation of the translation quality at both character
and word levels.
6. Post-Processing
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The results undergo Post-Processing for additional analysis:
Error Analysis: Identifies and categorizes errors in the output.
Loss Calculation: Measures discrepancies between predicted and actual outputs.
7. Output
The final output is Simple Bangla, generated from the input regional dialects.
Summary
The methodology follows these structured stages:
1. Dataset → Collection of Bangla dialects.
2. Preprocessing → Cleaning, tokenizing, and normalizing data.
3. Model → Use of mT5 and BanglaT5 for translation.
4. Translation → Transform regional dialects into Simple Bangla.
5. Evaluation → Measure performance using various metrics.
6. Post-Processing → Analyze errors and losses.
7. Output → Final translation result in Simple Bangla.
Let me know if you'd like more details on any specific part!
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