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README.md

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+---
+license: apache-2.0
+datasets:
+- jigsaw_toxicity_pred
+language:
+- en
+metrics:
+- perplexity
+---
+# Model Card for `gplus`
+
+This model is a `facebook/bart-large` fine-tuned on non-toxic comments from `jigsaw_toxicity_pred` dataset.
+Only a subset (20%) of the non-toxic comments were used for training this dataset.
+
+## Model Details
+
+This model is not intended to be used for plain inference, even though it is unlikely to predict toxic content.
+It is intended to be used as "utility model" for detecting and fixing toxic content as its token probability distributions will likely differ from comparable models trained/fine-tuned over toxic data.
+Its name `gplus` refers to the _G+_ model in [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
+
+### Model Description
+
+
+- **Developed by:** [tteofili]
+- **Shared by :** [tteofili]
+<!--- **Model type:** [More Information Needed]-->
+<!--- **Language(s) (NLP):** [More Information Needed]-->
+- **License:** [apache-2.0]
+- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
+<!--
+### Model Sources [optional]
+
+Provide the basic links for the model.
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+-->
+
+## Uses
+
+<!--
+Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model.
+
+### Direct Use
+
+This section is for the model use without fine-tuning or plugging into a larger ecosystem/app.
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+ This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app 
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+ This section addresses misuse, malicious use, and uses that the model will not work well for. 
+
+[More Information Needed]
+-->
+## Bias, Risks, and Limitations
+
+This model is fine-tuned over non-toxic comments from `jigsaw_toxicity_pred`, it is unlikely to produce toxic content.
+Nevertheless, this model should only be used in combination with other models for the sake of detecting / fixing toxic content, see for example [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
+
+<!--
+ This section is meant to convey both technical and sociotechnical limitations. 
+
+[More Information Needed]
+
+### Recommendations
+
+This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. 
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+ This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. 
+
+[More Information Needed]
+
+### Training Procedure 
+
+ This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. 
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+ **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision 
+
+#### Speeds, Sizes, Times [optional]
+
+- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. 
+
+[More Information Needed]
+-->
+## Evaluation
+
+ This section describes the evaluation protocols and provides the results. 
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+ This model was tested on `jigsaw_toxic_pred` testset.
+
+<!-- 
+#### Factors
+
+ These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. 
+
+[More Information Needed]
+-->
+#### Metrics
+
+ Model was evaluated using `perplexity` (on the MLM task).
+
+### Results
+
+Perplexity: _1.02_
+
+<!--
+#### Summary
+
+
+
+## Model Examination [optional]
+
+- Relevant interpretability work for the model goes here 
+
+[More Information Needed]
+
+## Environmental Impact
+
+ Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly 
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. 
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+ If relevant, include terms and calculations in this section that can help readers understand the model or model card. 
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

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+{
+  "_name_or_path": "facebook/bart-large",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "num_beams": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 50265
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 4,
+  "pad_token_id": 1,
+  "transformers_version": "4.31.0"
+}

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