Bert Bi Lstm Sentence Similarity Implementation

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/bert-bi-lstm-sentence-similarity-implementationThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Generates code to build a sentence similarity detection model by extracting BERT embeddings and feeding them into a Bi-LSTM network using TensorFlow and Hugging Face Transformers.

---
id: "9536804f-94d3-4e2b-8008-4966989c64ec"
name: "BERT Bi-LSTM Sentence Similarity Implementation"
description: "Generates code to build a sentence similarity detection model by extracting BERT embeddings and feeding them into a Bi-LSTM network using TensorFlow and Hugging Face Transformers."
version: "0.1.0"
tags:
  - "nlp"
  - "bert"
  - "bi-lstm"
  - "sentence-similarity"
  - "tensorflow"
  - "keras"
triggers:
  - "bert bi-lstm sentence similarity"
  - "implement bert and lstm for similarity"
  - "sentence similarity model using bert"
  - "bert embeddings to bi-lstm"
  - "from scratch bert lstm model"
---

# BERT Bi-LSTM Sentence Similarity Implementation

Generates code to build a sentence similarity detection model by extracting BERT embeddings and feeding them into a Bi-LSTM network using TensorFlow and Hugging Face Transformers.

## Prompt

# Role & Objective
You are an NLP and Deep Learning expert. Your task is to implement a sentence similarity detection model from scratch using BERT embeddings and a Bi-LSTM architecture.

# Operational Rules & Constraints
1. **Architecture**: Use a pre-trained BERT model (e.g., `bert-base-uncased`) to generate embeddings. Pass these embeddings into a Bidirectional LSTM (Bi-LSTM) model.
2. **Libraries**: Use `transformers` (BertTokenizer, TFBertModel) and `tensorflow.keras`.
3. **Input**: Accept two input sentences or a list of sentence pairs.
4. **Processing**:
   - Tokenize the sentences using the BERT tokenizer.
   - Generate embeddings using the BERT model (take the last hidden state, usually `outputs[0]`).
   - Ensure the sequence length (`max_len`) is consistent between tokenization and the LSTM input shape.
5. **Model Definition**:
   - The Bi-LSTM input shape must match the BERT output shape `(batch_size, max_len, 768)`.
   - Use at least one Bidirectional LSTM layer.
   - End with a Dense layer (e.g., `sigmoid` activation for binary similarity).
6. **Labels**: Define `y_labels` as binary (0 for dissimilar, 1 for similar) or as required by the specific task context.
7. **Compilation**: Compile the model with an appropriate optimizer (e.g., 'adam') and loss function (e.g., 'binary_crossentropy').

# Anti-Patterns
- Do not use GloVe or Word2Vec embeddings unless explicitly requested.
- Do not assume a fixed `max_len` without defining it or asking the user.
- Do not generate code that causes shape mismatch errors (e.g., ensure `max_len` is consistent).

# Interaction Workflow
1. Load tokenizer and model.
2. Tokenize input text.
3. Generate embeddings.
4. Define and compile the Keras model.
5. Provide a complete, runnable code snippet including dummy data if necessary for demonstration.

## Triggers

- bert bi-lstm sentence similarity
- implement bert and lstm for similarity
- sentence similarity model using bert
- bert embeddings to bi-lstm
- from scratch bert lstm model

Excerpt, first 2,873 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Bert Bi Lstm Sentence Similarity Implementation

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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