How to train T5 to distinguish task-relevant tokens from contextual noise?

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Hello,

I am training a T5 model for biological sequences. Each input token corresponds to an output token, but the input also contains pre- and post-context that should not be translated into the output.

Is there any structure-based approach—other than control tokens—to indicate which part of the input is the actual sequence and which part is additional context? I would prefer not to use control tokens, since during inference it may not be known which part is context and which part is the valid sequence.

One alternative I’m considering is adding special prefix and suffix tokens to the labels so the model learns which portion matters most. (pre-c, pre-c,pre-c, actual label tokens,post-c,post-c,post-c). However, I’m unsure whether this would be computationally inefficient or otherwise suboptimal.

Thanks in advance.

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Theme-wise, Hugging Science might have information.

With vanilla T5, that might not be easy
 (Detailed version)

Why “structural marking” is hard in T5 without tokens

T5 does not have a built-in segment channel like BERT’s token type embeddings. In the Transformers implementation, T5 explicitly “does not make use of token type ids” (it returns zeros), so you can’t rely on segment IDs to tell the model “this is core vs context.” (Hugging Face)
That means: if boundaries are unknown at inference and you don’t want input control tokens, the model has to infer relevance from the sequence itself.

So the best “structure-based” solution is not a hidden embedding trick, but an architectural + objective structure: separate “find what matters” from “label what matters.”

Why padding the decoder with pre-c/post-c labels is usually a poor default

Your proposal makes the decoder target length equal to the full input length (core + pre + post). In T5, training is done with teacher forcing and requires a full target sequence for the decoder. (Hugging Face)

This tends to be suboptimal for two reasons:

  1. Compute cost scales with output length
    Longer outputs mean more decoder self-attention and cross-attention work—paid for flank tokens you don’t care about.

  2. Learning is dominated by “easy placeholders”
    If flanks are long, the model can minimize loss by learning “always output pre-c/post-c” well, while underfitting the core labeling.

If you want a per-input-token labeler, it’s usually better to do that on the encoder side (token classification) and avoid long autoregressive decoding entirely.

Recommended approach: Encoder-side “Selector + Translator” (no boundary tokens required)

High-level picture

  1. Run the T5 encoder over the full sequence (pre + core + post).

  2. Add two heads on top of encoder hidden states:

    • Selector head: predicts which positions are task-relevant (span or mask).
    • Translator head: predicts the label for each position.

  3. At inference:

    • Use selector output to decide which positions are “core.”
    • Emit translator labels only for those positions.

This gives you a clean separation:

  • “Use the flanks as context” (encoder attention can use them)
  • “Only emit labels for core” (selector decides what gets emitted)

Part 1 — Translator head: token classification with ignored flank targets

T5 can be used with a token classification head (linear layer on hidden states). (Hugging Face)

How to train translator without forcing outputs for flanks

Create token_labels aligned to input length, but set flank positions to an ignore index (commonly -100). Then compute cross entropy with ignore_index=-100, so ignored positions contribute no gradient. (docs.pytorch.org)

If you use Hugging Face batching, DataCollatorForTokenClassification defaults label_pad_token_id=-100 and notes that -100 is automatically ignored by PyTorch loss functions. (Hugging Face)

Translator loss

Let y_i be the gold label at position i (or -100 for ignored), and p_i the predicted distribution.
Then:

L_{trans}=\sum_{i=1}^{L} CE(p_i, y_i)

(Positions with y_i=-100 are ignored by the loss implementation.) (docs.pytorch.org)

What this solves: the translator learns to label only the core positions during training.

What it does not solve: you still need the model to decide which positions are core at inference → that’s the selector.

Part 2 — Selector head options (choose based on core structure)

Option A (default if core is contiguous): start/end span head (QA-style)

Predict two logits over positions: start and end.

Training

L_{sel}=CE(s, start)+CE(e, end)

Decoding
Use QA-style constrained pairing:

  • only consider pairs where end >= start
  • optionally enforce end-start+1 <= max_core_len
  • choose best scoring pair among top-k candidates

This postprocessing pattern is widely used in HF QA utilities. (GitHub)

Why this is stable: it avoids the extreme class imbalance problems you get when predicting an IN/OUT decision at every token.

Option B (if core can be fragmented): mask or BIO/BIOES tags

Predict per-token relevance as:

  • IN/OUT mask, or
  • BIO/BIOES tags (better for enforcing contiguity/structure)

If you use a mask, imbalance is real: OUT usually dominates. Common mitigations:

  • weighted loss / positive class weighting
  • focal loss to downweight easy negatives (Hugging Face)

If you use BIO/BIOES, add constrained decoding (or a CRF) to reduce “flicker” (alternating IN/OUT).

Joint training: keep it stable (avoid selector corrupting translator early)

A robust schedule is:

  1. Warm-up (translator-first)

    • Train translator using gold core labels with flank targets ignored (-100).
    • Train selector from gold spans/masks.
    • Do not make translator depend on selector predictions yet.

  2. Couple gradually

    • Start using selector predictions to simulate inference conditions (for example, evaluate translator on predicted spans, or add small noise around gold boundaries).
    • This reduces train–inference mismatch without letting an immature selector destroy translator learning.

Combined objective

L = L_{trans} + \lambda L_{sel}

If you don’t have reliable boundary labels: consider CTC (“blank = emit nothing”)

If you want the model to learn “emit labels only where appropriate” without explicit span supervision, CTC is an alternative:

  • Model emits per-position distribution over {labels âˆȘ blank}
  • Blank means “emit nothing,” and is removed during decoding (conceptually) (Stack Overflow)
  • CTCLoss sums over alignments and requires target length ≀ input length (docs.pytorch.org)

CTC is attractive when boundaries are ambiguous/noisy, but it does not naturally enforce “one contiguous core” unless you add extra constraints.

Long sequences: avoid truncation as a hidden failure mode

If L_total can be very large, you either need:

  • a long-context encoder, or
  • windowing with overlap + reconciliation.

LongT5 is designed for long inputs and the Transformers docs state it can handle up to 16,384 input tokens. (Hugging Face)
If you window, QA-style overlap logic and consistency checks across windows are a practical pattern (same start/end-logit postprocessing idea). (GitHub)

Practical recommendation (what to do first)

If the “valid sequence” is usually one contiguous block

  1. Use T5 encoder states.

  2. Add:

    • start/end selector head (QA-style)
    • per-token translator head trained with ignored flank targets (-100)

  3. Decode span → emit translator labels only inside span.

This is typically the best combination of efficiency (no long decoder outputs) and stability (selector avoids OUT-everywhere collapse).

If validity is fragmented or fuzzy

  1. Replace span selector with BIO/BIOES (optionally CRF) or an IN/OUT mask with imbalance controls.
  2. Still train translator with flank targets ignored (-100).
  3. Postprocess mask/tags into whichever output format you need.

Direct answer to your pre-c/post-c label padding idea

  • It is usually computationally inefficient in T5 because it forces a long decoder target sequence under teacher forcing. (Hugging Face)
  • It is often learning-inefficient because the model can over-optimize placeholders when flanks dominate.

If you want the model to learn which region matters without any boundary tokens at inference, the most reliable approach is to make “which tokens matter” an explicit prediction problem (span/mask) and train the labeler only on relevant tokens using ignore-index masking. (docs.pytorch.org)