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README.md

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+---
+license: mit
+tags:
+- automated-planning
+- masked-language-modeling
+- bert
+---
+
+# BERT for Automated Planning (Logistics)
+
+This is a BERT model pretrained on Masked Language Modelling (MLM), specifically developed to tackle tasks related to Automated Planning within the Logistics domain. 
+
+You can find its full description, methodology, and experimental results in our paper: **[A Preliminary Study on BERT applied to Automated Planning](https://ceur-ws.org/Vol-3345/paper7_3460.pdf)**.
+
+## Usage
+
+You can easily load the model and the tokenizer using the Hugging Face `transformers` library:
+
+```python
+from transformers import BertForMaskedLM, PreTrainedTokenizerFast
+
+tokenizer = PreTrainedTokenizerFast.from_pretrained("lore-seri97/bert-logistics")
+model = BertForMaskedLM.from_pretrained("lore-seri97/bert-logistics")
+
+```
+
+## Citation
+
+If you use this model in your research, please cite our work using the following BibTeX entry:
+
+```bibtex
+@inproceedings{DBLP:conf/aiia/SerinaCGPS22,
+  author       = {Lorenzo Serina and
+                  Mattia Chiari and
+                  Alfonso Emilio Gerevini and
+                  Luca Putelli and
+                  Ivan Serina},
+  editor       = {Riccardo De Benedictis and
+                  Nicola Gatti and
+                  Marco Maratea and
+                  Andrea Micheli and
+                  Aniello Murano and
+                  Enrico Scala and
+                  Luciano Serafini and
+                  Ivan Serina and
+                  Alessandro Umbrico and
+                  Mauro Vallati},
+  title        = {A Preliminary Study on {BERT} applied to Automated Planning},
+  booktitle    = {Proceedings of the 10th Italian workshop on Planning and Scheduling
+                  {(IPS} 2022), {RCRA} Incontri {E} Confronti (RiCeRcA 2022), and the
+                  workshop on Strategies, Prediction, Interaction, and Reasoning in
+                  Italy {(SPIRIT} 2022) co-located with 21st International Conference
+                  of the Italian Association for Artificial Intelligence (AIxIA 2022),
+                  November 28 - December 2, 2022, University of Udine, Udine, Italy},
+  series       = {{CEUR} Workshop Proceedings},
+  volume       = {3345},
+  publisher    = {CEUR-WS.org},
+  year         = {2022},
+  url          = {[https://ceur-ws.org/Vol-3345/paper7](https://ceur-ws.org/Vol-3345/paper7)\_3460.pdf},
+  timestamp    = {Fri, 10 Mar 2023 16:23:01 +0100},
+  biburl       = {[https://dblp.org/rec/conf/aiia/SerinaCGPS22.bib](https://dblp.org/rec/conf/aiia/SerinaCGPS22.bib)},
+  bibsource    = {dblp computer science bibliography, [https://dblp.org](https://dblp.org)}
+}
+
+```
+
+---

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad_token_id": 3,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 67
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "pad_token_id": 3,
+  "transformers_version": "4.30.0.dev0"
+}

pytorch_model.bin

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special_tokens_map.json

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+  "sep_token": "[SEP]",
+  "goal_token": "[SG]",
+  "init_token": "[SI]",
+  "action_token": "[A]",
+  "unk_token": "[UNK]"
+}

tokenizer.json

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tokenizer_config.json

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+{
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+  "init_token": "[SI]",
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+  "tokenizer_class": "PreTrainedTokenizerFast",
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+  "unk_token": "[UNK]"
+}