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NSFW_text_classifier/README.md

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+---
+language: "en"
+tags:
+- distilroberta
+- sentiment
+- NSFW
+- inappropriate 
+- spam
+- twitter
+- reddit
+
+widget:
+- text: "I like you. You remind me of me when I was young and stupid."
+- text: "I see you’ve set aside this special time to humiliate yourself in public."
+- text: "Have a great weekend! See you next week!"
+
+---
+
+# Fine-tuned DistilRoBERTa-base for NSFW Classification
+
+# Model Description 
+
+DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). 
+
+The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert).
+
+It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
+
+# How to Use 
+
+```python
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis", model="michellejieli/NSFW_text_classification")
+classifier("I see you’ve set aside this special time to humiliate yourself in public.")
+```
+
+```python
+Output:
+[{'label': 'NSFW', 'score': 0.998853325843811}]
+```
+
+# Contact
+
+Please reach out to [[email protected]](mailto:[email protected]) if you have any questions or feedback.
+
+---

NSFW_text_classifier/config.json

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+{
+  "_name_or_path": "michellejieli/NSFW_text_classifier",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "SFW",
+    "1": "NSFW"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "NSFW": 1,
+    "SFW": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.2",
+  "vocab_size": 30522
+}

NSFW_text_classifier/pytorch_model.bin

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NSFW_text_classifier/special_tokens_map.json

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

NSFW_text_classifier/tokenizer_config.json

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

NSFW_text_classifier/training_args.bin

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