# Model Card for krishnagarg09/stance-detection-semeval2016

## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:

```
Input: Lord, You are my Hope! In You I will always trust.
Target: Atheism
Stance: AGAINST
```

The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available  at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.

Ref: https://aclanthology.org/S16-1003/ for more details about the dataset

- **Developed by:** Krishna Garg
- **Shared by [Optional]:** Krishna Garg
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** mit
- **Resources for more information:**
    - [Associated Paper](https://aclanthology.org/S16-1003/)

## Direct Use
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("krishnagarg09/stance-detection-semeval2016")
model = AutoModelForSequenceClassification.from_pretrained("krishnagarg09/stance-detection-semeval2016")

# load dataset
dataset = load_dataset("krishnagarg09/SemEval2016Task6")

# prepare input
text = dataset['test']['Tweet']
encoded_input = tokenizer(text, return_tensors='pt', add_special_tokens = True, max_length=128, padding=True, truncation=True)

# forward pass
output = model(**encoded_input)
```

## Dataset
The dataset is available  at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.
```
dataset = load_dataset("krishnagarg09/SemEval2016Task6")
```

## Training Details
```
optimizer: Adam
lr: 2e-5
loss: crossentropy
epochs: 5 (best weights chosen over validation)
batch_size: 32
```

### Preprocessing
Text lowercased, `#semst` tags removed, `p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED` removed using `tweet-preprocessor` package, normalization done using `emnlp_dict.txt` and `noslang_data.json`

## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST.

```
Precision: 62.69
Recall: 69.43
F1: 65.56
```

## Hardware
Nvidia RTX A5000 24GB

## Model Card Contact
krishnakantgarg@gmail.com
{}
transformers pytorch roberta text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# Model Card for krishnagarg09/stance-detection-semeval2016

## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:



The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available  at URL

Ref: URL for more details about the dataset

- Developed by: Krishna Garg
- Shared by [Optional]: Krishna Garg
- Model type: Language model
- Language(s) (NLP): en
- License: mit
- Resources for more information:
    - Associated Paper

## Direct Use


## Dataset
The dataset is available  at URL


## Training Details


### Preprocessing
Text lowercased, '#semst' tags removed, 'p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED' removed using 'tweet-preprocessor' package, normalization done using 'emnlp_dict.txt' and 'noslang_data.json'

## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST.



## Hardware
Nvidia RTX A5000 24GB

## Model Card Contact
krishnakantgarg@URL
# Model Card for krishnagarg09/stance-detection-semeval2016 ## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:



The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available  at URL

Ref: URL for more details about the dataset

- Developed by: Krishna Garg
- Shared by [Optional]: Krishna Garg
- Model type: Language model
- Language(s) (NLP): en
- License: mit
- Resources for more information:
    - Associated Paper ## Direct Use ## Dataset
The dataset is available  at URL ## Training Details ### Preprocessing
Text lowercased, '#semst' tags removed, 'p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED' removed using 'tweet-preprocessor' package, normalization done using 'emnlp_dict.txt' and 'noslang_data.json' ## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST. ## Hardware
Nvidia RTX A5000 24GB ## Model Card Contact
krishnakantgarg@URL
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for krishnagarg09/stance-detection-semeval2016 ## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:



The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available  at URL

Ref: URL for more details about the dataset

- Developed by: Krishna Garg
- Shared by [Optional]: Krishna Garg
- Model type: Language model
- Language(s) (NLP): en
- License: mit
- Resources for more information:
    - Associated Paper ## Direct Use ## Dataset
The dataset is available  at URL ## Training Details ### Preprocessing
Text lowercased, '#semst' tags removed, 'p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED' removed using 'tweet-preprocessor' package, normalization done using 'emnlp_dict.txt' and 'noslang_data.json' ## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST. ## Hardware
Nvidia RTX A5000 24GB ## Model Card Contact
krishnakantgarg@URL
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# Model Card for krishnagarg09/stance-detection-semeval2016## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.

Sample:



The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available  at URL

Ref: URL for more details about the dataset

- Developed by: Krishna Garg
- Shared by [Optional]: Krishna Garg
- Model type: Language model
- Language(s) (NLP): en
- License: mit
- Resources for more information:
    - Associated Paper## Direct Use## Dataset
The dataset is available  at URL## Training Details### Preprocessing
Text lowercased, '#semst' tags removed, 'p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED' removed using 'tweet-preprocessor' package, normalization done using 'emnlp_dict.txt' and 'noslang_data.json'## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST.## Hardware
Nvidia RTX A5000 24GB## Model Card Contact
krishnakantgarg@URL