# Huggingface classification struggling with prediction

**URL:** <https://discuss.huggingface.co/t/huggingface-classification-struggling-with-prediction/16501>\
**Category:** 🤗Transformers\
**Created:** [April 5, 2022, 4:50pm UTC](https://discuss.huggingface.co/t/huggingface-classification-struggling-with-prediction/16501 "2022-04-05T16:50:37Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![nitempe](https://avatars.discourse-cdn.com/v4/letter/n/54ee81/32.png) [@nitempe](https://discuss.huggingface.co/u/nitempe)\
**Post date:** [April 5, 2022, 4:50pm UTC](https://discuss.huggingface.co/t/huggingface-classification-struggling-with-prediction/16501/1 "2022-04-05T16:50:37Z")

</div>

I am fine tuning longformer and then making prediction using `TextClassificationPipeline` and `model(**inputs)` methods. I am not sure why I get different results

```
import pandas as pd
import datasets
from transformers import LongformerTokenizerFast, LongformerForSequenceClassification, Trainer, TrainingArguments, LongformerConfig
import torch.nn as nn
import torch
from torch.utils.data import DataLoader#Dataset, 
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from tqdm import tqdm
#import wandb
import os
from datasets import Dataset
from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification

tokenizer = LongformerTokenizerFast.from_pretrained('folder_path/', max_length = maximum_len)

```

Loading the fine tuned model from a saved location. Using original tokenizer

```
saved_location='c:/xyz'
model_saved=AutoModelForSequenceClassification.from_pretrained(saved_location)
pipe = TextClassificationPipeline(model=model_saved, tokenizer=tokenizer, device=0)#tokenizer_saved, padding=True, truncation=True)
prediction = pipe(["The text to predict"], return_all_scores=True)
prediction
[[{'label': 'LABEL_0', 'score': 0.7107483148574829},
  {'label': 'LABEL_1', 'score': 0.2892516553401947}]]

```

2nd method

```
inputs = tokenizer("The text to predict", return_tensors="pt").to(device)
outputs = model_saved(**inputs)#, labels=labels)
print (outputs['logits'])
#tensor([[0.4552, -0.4438]], device='cuda:0', grad_fn=<AddmmBackward0>)
torch.sigmoid(outputs['logits'])
#tensor([[0.6119, 0.3908]], device='cuda:0', grad_fn=<SigmoidBackward0>)

```

`AutoModelForSequenceClassification` returns probabilities `0.71 and 0.29`. When I look at the 2nd method. It returns logits `0.4552, -0.4438` which convert to probabilities `0.6119, 0.3908`