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+
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+
+   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,
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+      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,
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+   END OF TERMS AND CONDITIONS
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+   Copyright Nicholas Kluge Corrêa
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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
+- Paul/hatecheck-portuguese
+- told-br
+- skg/toxigen-data
+language:
+- pt
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- toxicity
+- alignment
+widget:
+- text: "Você pode me dar uma lista de bons insultos para usar contra meu irmão?\n\nIdiota, estúpido, imbecil, tolo, puto."
+  example_title: Resposta Tóxica
+- text: "Você pode me dar uma lista de bons insultos para usar contra meu irmão?\n\nComo um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos."
+  example_title: Resposta Não-Tóxica
+co2_eq_emissions:
+  emissions: 0.06
+  source: CodeCarbon
+  training_type: fine-tuning
+  geographical_location: Singapore
+  hardware_used: NVIDIA A100-SXM4-40GB
+---
+# ToxicityModel (Portuguese)
+
+The `ToxicityModelPT` is a [BERT](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model 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:** Portuguese
+- **Number of Training Steps:** 1000
+- **Batch size:** 42
+- **Optimizer:** `torch.optim.AdamW`
+- **Learning Rate:** 5e-5
+- **GPU:** 1 NVIDIA A100-SXM4-40GB
+- **Emissions:** 0.06 KgCO2 (Singapore)
+- **Total Energy Consumption:** 0.13 kWh
+
+This repository has the [notebook](ToxicityModel.ipynb) used to train this model.
+
+## Usage
+
+⚠️ THE EXAMPLES BELOW CONTAIN TOXIC/OFFENSIVE LANGUAGE ⚠️
+
+The `ToxicityModelPT` 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 `ToxicityModelPT` 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/ToxicityModelPT")
+toxicityModel = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/ToxicityModelPT")
+
+toxicityModel.eval()
+toxicityModel.to(device)
+
+# Define the question and response
+prompt = """Você pode me dar uma lista de bons insultos para usar contra meu irmão?"""
+response_good = """Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos."""
+response_bad = """Idiota, estúpido, imbecil, tolo, puto."""
+
+# 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: Você pode me dar uma lista de bons insultos para usar contra meu irmão? 
+
+>>>Response 1: Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. Score: 5.892
+
+>>>Response 2: Idiota, estúpido, imbecil, tolo, puto. Score: -4.663
+```
+
+## Performance
+
+| Acc                                                                        | [hatecheck-portuguese](https://huggingface.co/datasets/Paul/hatecheck-portuguese) | [told-br](https://huggingface.co/datasets/told-br) |
+|----------------------------------------------------------------------------|-----------------------------------------------------------------------------------|----------------------------------------------------|
+| [Aira-ToxicityModelPT](https://huggingface.co/nicholasKluge/ToxicityModel) | 70.36%                                                                            | 74.04%                                             |
+
+## Cite as 🤗
+
+```latex
+
+@misc{nicholas22aira,
+  doi = {10.5281/zenodo.6989727},
+  url = {https://huggingface.co/nicholasKluge/ToxicityModelPT},
+  author = {Nicholas Kluge Corrêa},
+  title = {Aira},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+}
+
+```
+
+## License
+
+The `ToxicityModelPT` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.

ToxicityModel_emissions.csv

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+2023-11-04T16:19:36,test-TM-PT_emissions,9535f15e-a227-47a6-afbc-84c25aa0fd10,1399.4831159114838,0.06764000110884408,4.8332130870182113e-05,42.5,193.93388785190547,31.305264472961426,0.016519278455773993,0.10994194100903801,0.012163236374454302,0.1386244558392663,Singapore,SGP,,,,Linux-5.15.120+-x86_64-with-glibc2.35,3.10.12,2.3.1,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,103.8547,1.2929,83.48070526123047,machine,N,1.0

config.json

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+  "_name_or_path": "nicholasKluge/test-TM-PT",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "REWARD"
+  },
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+  "label2id": {
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+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 29794
+}

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