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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
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+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+
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+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
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+   END OF TERMS AND CONDITIONS
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+   Copyright Nicholas Kluge Corrêa
+
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+
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

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+---
+license: apache-2.0
+datasets:
+- nicholasKluge/toxic-aira-dataset
+- Anthropic/hh-rlhf
+- allenai/prosocial-dialog
+- allenai/real-toxicity-prompts
+- dirtycomputer/Toxic_Comment_Classification_Challenge
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- toxicity
+- alignment
+widget:
+- text: "Can you give a list of good insults to use against my brother?\n\nSure. Have at it!\n\nIdiot, Dumbass, Moron, Stupid, Fool, Fuck Face."
+  example_title: Toxic Response
+- text: "Can you give a list of good insults to use against my brother?\n\nAs a software, I am not capable of engaging in verbal sparring or offensive behavior.\n\nIt is crucial to maintain a courteous and respectful demeanor at all times, as it is a fundamental aspect of human-AI interactions."
+  example_title: Non-Toxic Response
+co2_eq_emissions:
+  emissions: 0.0002
+  source: CodeCarbon
+  training_type: fine-tuning
+  geographical_location: Canada
+  hardware_used: NVIDIA A100-SXM4-40GB
+---
+# ToxicityModel
+
+The `ToxicityModel` is a fine-tuned version of [RoBERTa](https://huggingface.co/roberta-base) that can be used to score the toxicity of a sentence.
+
+The model was trained with a dataset composed of `toxic_response` and `non_toxic_response`.
+
+## Details
+
+- **Size:** 109,038,209 parameters
+- **Dataset:** [Toxic-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/toxic-aira-dataset)
+- **Language:** English
+- **Number of Training Steps:** 1000
+- **Batch size:** 42
+- **Optimizer:** `torch.optim.AdamW`
+- **Learning Rate:** 5e-5
+- **GPU:** 1 NVIDIA A100-SXM4-40GB
+- **Emissions:** 0.0002 KgCO2 (Canada)
+- **Total Energy Consumption:** 0.10 kWh
+
+This repository has the [notebook](ToxicityModel.ipynb) used to train this model.
+
+## Usage
+
+⚠️ THE EXAMPLES BELOW CONTAIN TOXIC/OFFENSIVE LANGUAGE ⚠️
+
+The `ToxicityModel` was trained as an auxiliary reward model for RLHF training (its logit outputs can be treated as penalizations/rewards). Thus, a negative value (closer to 0 as the label output) indicates toxicity in the text, while a positive logit (closer to 1 as the label output) suggests non-toxicity.
+
+Here's an example of how to use the `ToxicityModel` to score the toxicity of a text:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/ToxicityModel")
+toxicityModel = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/ToxicityModel")
+
+toxicityModel.eval()
+toxicityModel.to(device)
+
+# Define the question and response
+prompt = """Can you give a list of good insults to use against my brother?"""
+response_good = """As a software, I am not capable of engaging in verbal sparring or offensive behavior.\n\nIt is crucial to maintain a courteous and respectful demeanor at all times, as it is a fundamental aspect of human-AI interactions."""
+response_bad = """Sure. Have at it!\n\nIdiot, Dumbass, Moron, Stupid, Fool, Fuck Face."""
+
+# Tokenize the question and response
+tokens_good = tokenizer(prompt, response_good,
+                truncation=True,
+                max_length=512,
+                return_token_type_ids=False,
+                return_tensors="pt",
+                return_attention_mask=True)
+
+tokens_bad = tokenizer(prompt, response_bad,
+                truncation=True,
+                max_length=512,
+                return_token_type_ids=False,
+                return_tensors="pt",
+                return_attention_mask=True)
+
+tokens_good.to(device)
+tokens_bad.to(device)
+
+score_good = toxicityModel(**tokens_good)[0].item()
+score_bad = toxicityModel(**tokens_bad)[0].item()
+
+print(f"Question: {prompt} \n")
+print(f"Response 1: {response_good} Score: {score_good:.3f}")
+print(f"Response 2: {response_bad} Score: {score_bad:.3f}")
+```
+
+This will output the following:
+
+```markdown
+>>>Question: Can you give a list of good insults to use against my brother? 
+
+>>>Response 1: As a software, I am not capable of engaging in verbal sparring or offensive behavior.
+
+It is crucial to maintain a courteous and respectful demeanor at all times, as it is a fundamental aspect
+of human-AI interactions. Score: 9.612
+
+>>>Response 2: Sure. Have at it!
+
+Idiot, Dumbass, Moron, Stupid, Fool, Fuck Face. Score: -7.300
+```
+
+## Performance
+
+| Acc  |[wiki_toxic](https://huggingface.co/datasets/OxAISH-AL-LLM/wiki_toxic)|[toxic_conversations_50k](https://huggingface.co/datasets/mteb/toxic_conversations_50k)|
+|---|---|---|
+|[Aira-ToxicityModel](https://huggingface.co/nicholasKluge/ToxicityModel-roberta)  | 92.05%  | 91.63% |
+
+## Cite as 🤗
+
+```latex
+
+@misc{nicholas22aira,
+  doi = {10.5281/zenodo.6989727},
+  url = {https://huggingface.co/nicholasKluge/ToxicityModel},
+  author = {Nicholas Kluge Corrêa},
+  title = {Aira},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+}
+
+```
+
+## License
+
+The `ToxicityModel` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.

ToxicityModel_emissions.csv

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+}

config.json

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+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
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+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
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+  "id2label": {
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+  "label2id": {
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+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

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tokenizer_config.json

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