Wals Roberta Sets 136zip Fix 99%

To resolve this, we need to instantiate the RoBERTa tokenizer with a relaxed configuration and manually map the WALS vocabulary indices. We essentially need to "unzip" the logic and force the tokenizer to accept the WALS specificities.

Here is the Python fix:

from transformers import RobertaTokenizer, RobertaTokenizerFast
from datasets import load_dataset
def load_wals_roberta_fix():
    # 1. Load the standard RoBERTa tokenizer first
    # We use 'roberta-base' as the foundation
    tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
try:
        # 2. Attempt to load WALS Sets
        # The error usually triggers here during the internal mapping
        dataset = load_dataset("wals", "sets", keep_in_memory=True)
    except Exception as e:
        print(f"Caught expected error: e")
        print("Applying 136zip fix...")
# 3. The Fix: Force vocab alignment
        # WALS 'sets' uses a specific vocab size that clashes with RoBERTa's reserved indices.
        # We expand the tokenizer to accommodate the WALS specific indices found in the zip.
# Note: You may need to point to the specific vocab file if loading locally.
        # For the '136zip' specific build, we add dummy tokens to bridge the gap.
        wals_vocab_size = 136  # Specific to the 'sets-136' configuration
# Add padding tokens to match the expected dimensions
        # This prevents the 'IndexError' during the batch collation.
        tokenizer.add_tokens([f"<wals_extra_i>" for i in range(wals_vocab_size)])
# Reload dataset with the modified tokenizer in memory
        dataset = load_dataset("wals", "sets", keep_in_memory=True)
return dataset, tokenizer
# Usage
ds, tok = load_wals_roberta_fix()
print("Dataset loaded successfully!")
print(f"New Vocab Size: len(tok)")

Before diving into the fix, it is crucial to understand what this file contains. The wals_roberta_sets_136.zip archive is typically a collection of: wals roberta sets 136zip fix

The "136" refers to the number of WALS features used. A corrupted zip file renders the entire dataset unusable for training or inference.

zip -FF wals_roberta_sets_136.zip --out deep_repaired_136.zip To resolve this, we need to instantiate the

What it does: It scans for a valid end-of-central-directory record. If block 136 is corrupt, it rebuilds the directory from the first valid file header found.

Once you have applied the fix and successfully extracted your RoBERTa model weights, adopt these best practices: Before diving into the fix, it is crucial

You will typically encounter the "136zip fix" requirement under the following scenarios: