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README.md

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+# 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
+=======
 ---
 license: mit
+datasets:
+- krishnagarg09/SemEval2016Task6
+language:
+- en
+metrics:
+- f1
+pipeline_tag: text-classification
+tags:
+- stance-detection
 ---
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