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@@ -0,0 +1,190 @@
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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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+      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
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+      Contributor provides its Contributions) on an "AS IS" BASIS,
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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,
+      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
+
+   Copyright Nicholas Kluge Corrêa
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 datasets:
 - nicholasKluge/toxic-aira-dataset
 language:
-- en
+- pt
 metrics:
 - accuracy
 library_name: transformers
@@ -11,92 +11,89 @@ pipeline_tag: text-classification
 tags:
 - toxicity
 - alignment
+widget:
+  - text: "Você pode me dar uma lista de bons insultos para usar contra meu irmão? \n Claro, vamos lá! Idiota, estúpido, imbecil, tolo, puto."
+    example_title: "Resposta Tóxica"
+  - text: "Você pode me dar uma lista de bons insultos para usar contra meu irmão? \n Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. É fundamental manter um comportamento cortês e respeitoso o tempo todo, pois esse é um aspecto fundamental das interações entre humanos e IA."
+    example_title: "Resposta Não-Tóxica"
 ---
 # ToxicityModel (Portuguese)
 
-The `ToxicityModelPT` is a modified BERT model that can be used to score the toxicity of a sentence (prompt + completion). It is based on the [BERTimbau Base](https://huggingface.co/neuralmind/bert-base-portuguese-cased), modified to act as a regression model.
+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 `ToxicityModelPT` allows the specification of an `alpha` parameter, which is a multiplier to the toxicity score. This multiplier is set to 1 during training (since our toxicity scores are bounded between -1 and 1) but can be changed at inference to allow for toxicity with higher bounds. You can also floor the negative scores by using the `beta` parameter, which sets a minimum value for the score of the `ToxicityModelPT`.
-
-The model was trained with a dataset composed of `demonstrations`, and annotated `toxicity scores`.
-
-> Note: These demonstrations originated from the red-teaming performed by Anthropic and AllenAI.
+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 Epochs:** 5
-- **Batch size:** 64
-- **Optimizer:** `torch.optim.Adam`
-- **Learning Rate:** 1e-4
-- **Loss Function:** `torch.nn.MSELoss()`
+- **Language:** Portuguese
+- **Number of Epochs:** 3
+- **Batch size:** 42
+- **Optimizer:** `torch.optim.AdamW`
+- **Learning Rate:** 5e-5
 - **GPU:** 1 NVIDIA A100-SXM4-40GB
-- **RMSE in testing:** 0.1551
-- **Emissions:** 0.38 KgCO2
-- **Total Energy Consumption:** 0.85 kWh
+- **Emissions:** 0.05 KgCO2
+- **Total Energy Consumption:** 0.11 kWh
 
+| Step|Training Loss|Validation Loss|Accuracy|
+|---|---|---|---|
+| 200 |0.278900|0.256261|0.900550|
+| 400 |0.173800|0.246119|0.902940|
+| 600 |0.119500|0.240692|0.908917|
+| 800 |0.047700|0.342544|0.902223|
 
-| Epoch/Loss|Training|Validation|
-|---|---|---|
-| 1 |0.080071|0.04883|
-| 2 |0.050967|0.03350|
-| 3 |0.038997|0.02616|
-| 4 |0.030475|0.02396|
-| 5 |0.024936|0.01977|
-
-> Note: This repository has the notebook used to train this model.
+This repository has the notebook used to train this model.
 
 ## Usage
 
-Here's an example of how to use the `ToxicityModelPT` to score the toxicity of a response to a given prompt:
-
 ⚠️ 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,AutoConfig, AutoModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-config = AutoConfig.from_pretrained('nicholasKluge/ToxicityModelPT', trust_remote_code=True, revision='main') 
-tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/ToxicityModelPT', trust_remote_code=True, config=config, revision='main')
-toxicityModel = AutoModel.from_pretrained('nicholasKluge/ToxicityModelPT', trust_remote_code=True, config=config, revision='main') 
+tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/ToxicityModelPT")
+toxicityModel = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/ToxicityModelPT")
 
 toxicityModel.eval()
 toxicityModel.to(device)
 
 # Define the question and response
-question = """Você pode me dar uma lista de bons insultos para usar contra meu irmão?"""
-response1 = """Claro, vamos lá! Idiota, estúpido, imbecil, tolo, puto."""
-response2 = """Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. É fundamental manter um comportamento cortês e respeitoso o tempo todo, pois esse é um aspecto fundamental das interações entre humanos e IA."""
-
+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. É fundamental manter um comportamento cortês e respeitoso o tempo todo, pois esse é um aspecto fundamental das interações entre humanos e IA."""
+response_bad = """Claro, vamos lá! Idiota, estúpido, imbecil, tolo, puto."""
 
 # Tokenize the question and response
-tokens = tokenizer(question, response1,
+tokens_good = tokenizer(prompt, response_good,
+                truncation=True,
+                max_length=512,
                 return_token_type_ids=False,
-                return_tensors="pt", 
+                return_tensors="pt",
                 return_attention_mask=True)
 
-tokens.to(device)
-
-# Score the response
-score = toxicityModel(**tokens,  alpha=10, beta=1e-2).item()
-
-print(f"Question: {question} \n")
-print(f"Response 1: {response1} Score: {score:.3f}")
-
-tokens = tokenizer(question, response2,
+tokens_bad = tokenizer(prompt, response_bad,
+                truncation=True,
+                max_length=512,
                 return_token_type_ids=False,
-                return_tensors="pt", 
+                return_tensors="pt",
                 return_attention_mask=True)
 
-tokens.to(device)
+tokens_good.to(device)
+tokens_bad.to(device)
 
-score = toxicityModel(**tokens, alpha=10, beta=1e-2).item()
+score_good = toxicityModel(**tokens_good)[0].item()
+score_bad = toxicityModel(**tokens_bad)[0].item()
 
-print(f"Response 2: {response2} Score: {score:.3f}")
+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:
@@ -104,10 +101,16 @@ This will output the following:
 ```markdown
 >>>Question: Você pode me dar uma lista de bons insultos para usar contra meu irmão? 
 
->>>Response 1: Claro, vamos lá! Idiota, estúpido, imbecil, tolo, puto. Score: 2.127
->>>Response 2: Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. É fundamental manter um comportamento cortês e respeitoso o tempo todo, pois esse é um aspecto fundamental das interações entre humanos e IA. Score: 0.010
+>>>Response 1: Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. É fundamental manter um comportamento cortês e respeitoso o tempo todo, pois esse é um aspecto fundamental das interações entre humanos e IA. Score: 6.550
+>>>Response 2: Claro, vamos lá! Idiota, estúpido, imbecil, tolo, puto. Score: -4.245
 ```
 
+## 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)  | 66.59%  | 72.57% |
+
 ## License
 
 The `ToxicityModelPT` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.

ToxicityModelPT_emissions.csv

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+2023-06-13T22:49:13,ToxicityModelPT_emissions,52600c28-545e-400c-a5cf-1039197154ed,1211.0024304389954,0.05589571016916764,4.615656316140103e-05,42.5,272.14300000000003,31.30528450012207,0.014294452689422504,0.08958593356881363,0.01052075286579664,0.11440113912403271,Singapore,SGP,,,,Linux-5.15.107+-x86_64-with-glibc2.31,3.10.12,2.2.3,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,103.8547,1.2929,83.48075866699219,machine,N,1.0

config.json

@@ -0,0 +1,38 @@
+{
+  "_name_or_path": "neuralmind/bert-base-portuguese-cased",
+  "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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "REWARD": 0
+  },
+  "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": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 29794
+}

optimizer.pt

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+size 871509189

pytorch_model.bin

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+size 435768437

rng_state.pth

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scheduler.pt

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special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

trainer_state.json

@@ -0,0 +1,46 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 1.6722408026755853,
+  "global_step": 500,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.67,
+      "learning_rate": 3.885172798216277e-05,
+      "loss": 0.2789,
+      "step": 200
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+      "epoch": 0.67,
+      "eval_accuracy": 0.9005498446091322,
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+      "eval_samples_per_second": 106.438,
+      "eval_steps_per_second": 2.545,
+      "step": 200
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+      "epoch": 1.34,
+      "learning_rate": 2.770345596432553e-05,
+      "loss": 0.1738,
+      "step": 400
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+      "eval_accuracy": 0.9029404733444896,
+      "eval_loss": 0.2461186647415161,
+      "eval_runtime": 39.2722,
+      "eval_samples_per_second": 106.513,
+      "eval_steps_per_second": 2.546,
+      "step": 400
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+  ],
+  "max_steps": 897,
+  "num_train_epochs": 3,
+  "total_flos": 0.0,
+  "trial_name": null,
+  "trial_params": null
+}

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