Comments (1)
Oh good catch. I 100% remember running the script, and it worked fine ... not sure what happened to it afterwards. But it works again and should be fixe now. Many thanks.
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Related Issues (20)
- Missing encoder.json and vocab.bpe for running bpe_openai_gpt2 (02_bonus_bytepair-encoder/compare-bpe-tiktoken.ipynb) HOT 2
- stride value caused skipping one word HOT 2
- Make it clear in REAME.md what this repository is for HOT 1
- requirements.txt HOT 3
- Incorrect code output in the book (2.2 Tokenizing text) HOT 4
- Encoding/decoding transformation of the text (2.3 Converting tokens into token IDs) HOT 1
- Solution of Excercise 2.1 is included in both main code and solution notebooks (2.5 Byte pair encoding) HOT 1
- Several package requirements from bonus material are not specified in requirements.txt (Tokenizers comparison) HOT 1
- Question about number of tokens in ChatGPT (2.5 Byte pair encoding) HOT 1
- Inconsistencies between the code in the book and the notebooks (2.6 Data sampling with a sliding window) HOT 7
- Output of the cell without variable specified (Embedding Layers and Linear Layers) HOT 1
- Wrong number of token ids specified in the notebook (2.7 Creating token embeddings) HOT 1
- Incorrect description of function torch.arange() (2.8 Encoding word positions) HOT 1
- Inconsistencies in output for dropout section (3.5.2 Masking additional attention weights with dropout) HOT 1
- Probably a typo in multi-head attention description (3.6.1 Stacking multiple single-head attention layers) HOT 1
- Solution for Exercise 3.2 is included in the notebook with main code (3.6.1 Stacking multiple single-head attention layers) HOT 1
- Question about implementation of CausalAttention class (3.5.3 Implementing a compact causal self-attention class) HOT 6
- Inconsistencies in unsqueeze operation description in the book and in notebook and its necessity (3.6.2 Implementing multi-head attention with weight splits) HOT 4
- Solution for Exercise 3.3 is included in the notebook with main code (3.6.2 Implementing multi-head attention with weight splits) HOT 1
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