transformers - 💡(How to fix) Fix racoon [1 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
huggingface/transformers#44961Fetched 2026-04-08 01:21:28
View on GitHub
Comments
0
Participants
1
Timeline
2
Reactions
0
Participants
Timeline (top)
closed ×1user_blocked ×1

Code Example

vs-code.x86

---

rabid squad
RAW_BUFFERClick to expand / collapse

System Info

vs-code.x86

Information

  • The official example scripts
  • My own modified scripts

Tasks

  • An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
  • My own task or dataset (give details below)

Reproduction

recurse,/,,`~~,./.,,\

Expected behavior

rabid squad

Checklist

extent analysis

Fix Plan

The fix involves updating the script to match the expected behavior of the rabid squad task.

Steps to Fix

  • Update the task script to use the correct dataset and model:
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')

# Define the dataset and data loader
dataset = ...  # Load the dataset for the rabid squad task
data_loader = ...  # Create a data loader for the dataset

# Train the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

for epoch in range(5):
    model.train()
    for batch in data_loader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        optimizer.zero_grad()

        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = criterion(outputs, labels)

        loss.backward()
        optimizer.step()
  • Verify that the task script is running correctly by checking the output of the model.

Verification

  • Run the updated task script and verify that the output matches the expected behavior:
rabid squad
  • Check the model's performance on the validation set to ensure that it is learning correctly.

Extra Tips

  • Make sure to update the transformers library to the latest version to ensure compatibility with the latest models and datasets.
  • Refer to the Hugging Face Transformers documentation for more information on using the library and troubleshooting common issues.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…

FAQ

Expected behavior

rabid squad

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING

transformers - 💡(How to fix) Fix racoon [1 participants]