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.
npx skills add ECNU-ICALK/AutoSkill --skill bert-bi-lstm-sentence-similarity-implementation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Bidirectiona
- Prompt
- Triggers
What does the BERT Bi-LSTM Sentence Similarity Implementation skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill bert-bi-lstm-sentence-similarity-implementation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.