Initial upload of sarcasm-detector

.gitattributes

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 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language: "en"
+tags:
+- bert
+- sarcasm-detection
+- text-classification
+widget:
+- text: "CIA Realizes It's Been Using Black Highlighters All These Years."
+---
+
+# English Sarcasm Detector
+
+English Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on [bert-base-uncased](https://huggingface.co/bert-base-uncased) and the training data consists of ready-made dataset available on Kaggle.
+
+
+<b>Labels</b>: 
+0 -> Not Sarcastic;
+1 -> Sarcastic
+
+
+## Source Data
+
+Datasets:
+- English language data: [Kaggle: News Headlines Dataset For Sarcasm Detection](https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection).
+
+## Training Dataset
+- [helinivan/sarcasm_headlines_multilingual](https://huggingface.co/datasets/helinivan/sarcasm_headlines_multilingual)
+
+## Codebase:
+- Git Repo: [Official repository](https://github.com/helinivan/multilingual-sarcasm-detector).
+
+---
+
+## Example of classification
+
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import string
+
+def preprocess_data(text: str) -> str:
+   return text.lower().translate(str.maketrans("", "", string.punctuation)).strip()
+
+MODEL_PATH = "helinivan/english-sarcasm-detector"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+text = "CIA Realizes It's Been Using Black Highlighters All These Years."
+tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=256, return_tensors="pt")
+output = model(**tokenized_text)
+probs = output.logits.softmax(dim=-1).tolist()[0]
+confidence = max(probs)
+prediction = probs.index(confidence)
+results = {"is_sarcastic": prediction, "confidence": confidence}
+
+```
+
+Output: 
+
+```
+{'is_sarcastic': 1, 'confidence': 0.9337034225463867}
+```
+
+## Performance
+| Model-Name | F1 | Precision | Recall | Accuracy 
+| ------------- |:-------------| -----| -----| ----| 
+| [helinivan/english-sarcasm-detector ](https://huggingface.co/helinivan/english-sarcasm-detector)| **92.38** | 92.75 | 92.38 | 92.42
+| [helinivan/italian-sarcasm-detector ](https://huggingface.co/helinivan/italian-sarcasm-detector) | 88.26 | 87.66 | 89.66 | 88.69
+| [helinivan/multilingual-sarcasm-detector ](https://huggingface.co/helinivan/multilingual-sarcasm-detector) | 87.23 | 88.65 | 86.33 | 88.30
+| [helinivan/dutch-sarcasm-detector ](https://huggingface.co/helinivan/dutch-sarcasm-detector) | 83.02 | 84.27 | 82.01 | 86.81

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "bert-base-uncased",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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