# Faster way to apply a model to dataframe

**URL:** <https://discuss.huggingface.co/t/faster-way-to-apply-a-model-to-dataframe/15258>\
**Category:** Beginners\
**Created:** [March 2, 2022, 12:59pm UTC](https://discuss.huggingface.co/t/faster-way-to-apply-a-model-to-dataframe/15258 "2022-03-02T12:59:05Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![thelara](https://avatars.discourse-cdn.com/v4/letter/t/e9bcb4/32.png) [@thelara](https://discuss.huggingface.co/u/thelara)\
**Post date:** [March 2, 2022, 12:59pm UTC](https://discuss.huggingface.co/t/faster-way-to-apply-a-model-to-dataframe/15258/1 "2022-03-02T12:59:06Z")

</div>

I have a pandas dataframe with 9m rows. Each one of them has a small comment (like a tweet). I would like to create a new column with label/score of a sentiment analysis. I’m trying to do that using `apply` function of pandas dataframe.

```python

model_name = 'finiteautomata/bertweet-base-sentiment-analysis'
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, normalization=True)
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)

def apply_model(row):
    
    batch = tokenizer(row.text, truncation=True, return_tensors='pt')
    
    with torch.no_grad():
        outputs = model(**batch)
        predictions = F.softmax(outputs.logits, dim=1)
        labels = torch.argmax(predictions, dim=1)
        label = [model.config.id2label[label_id] for label_id in labels.tolist()][0]
        score = torch.topk(predictions,1)[0].item()
    return [label, score]

df['SA'] = df.progress_apply(apply_model, axis=1)

```

The estimated time to conclusion (TQDM) is around 62 hours, lol.

How can i speed up this process?