Adding model and first version of the readme

README.md

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 ---
 license: cc-by-nc-sa-4.0
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+
+# Inclusively Rewriting model
+
+This model is an Italian sequence-to-sequence model fine-tuned from the [IT5-large](https://huggingface.co/gsarti/it5-large) for the task of inclusive language rewriting.
+
+It has been trained to analyze and rewrite sentences in Italian to make them more inclusive (if needed).
+
+For example, the sentence `I professori devono essere preparati` (The professors must be prepared) is rewritten as `Il personale docente deve essere preparato` (The teaching staff must be prepared).
+
+## Training data
+
+The model has been trained on a dataset containing a total of 4705 pairs of sentences, each pair containing an inclusive and a non-inclusive sentence. The dataset has been split as follows:
+- Training set: 3764 pairs
+- Validation set: 470 pairs
+- Test set: 471 pairs
+
+We also leverage a small set of synthetic data (generated using a set of rules) to improve the model's performance on the test set.
+The training is so performed on a total of 3764 + 75 = **3839** pairs.
+
+The data collection has been manually annotated by experts in the field of inclusive language (dataset is not publicly available yet).
+
+## Training procedure
+
+The model has been fine-tuned from the [Italian BERT model](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) using the following hyperparameters:
+- `max_length`: 128
+- `batch_size`: 8
+- `learning_rate`: 5e-5
+- `warmup_steps`: 500
+- `epochs`: 25 (best model is selected based on validation `BLEU` score)
+- `optimizer`: AdamW
+
+## Evaluation results
+
+The model has been evaluated on the test set and obtained the following results:
+
+| Model | BLEU | ROUGE-2 F1 | Human Correct | Human Partial (L) | Human Incorrect (L) |
+|-------|------|------------|---------------|---------------|-----------------|
+| IT5 (no synth. data) | 80.32 | 87.17 | 64.76 | 15.71 | 19.52 |
+| **This**             | 80.79 | 87.47 | 69.52 | 17.14 | 13.22 |
+
+(L) in the metric indicates "Lower is better".
+The comparison with the same version of the model without synthetic data shows that the synthetic data is useful to improve the model's performance on the test set.
+Other comparisons can be found in the [paper](#citation).
+
+## Citation
+
+If you use this model, please make sure to cite the following papers:
+
+**Demo paper**:
+
+```bibtex
+
+```
+
+**Main paper**:
+
+```bibtex
+
+```

config.json

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+{
+  "_name_or_path": "gsarti/it5-large",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2816,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "use_cache": true,
+  "vocab_size": 32103
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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training_args.bin

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