model update

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,138 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: it
+datasets:
+- lmqg/qg_itquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+widget:
+- text: "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
+  example_title: "Question Generation Example 1" 
+- text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa."
+  example_title: "Question Generation Example 2" 
+- text: "il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
+  example_title: "Question Generation Example 3" 
+model-index:
+- name: vocabtrimmer/mt5-small-trimmed-it-itquad-qg
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_itquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 7.17
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 21.78
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 17.45
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 80.56
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 56.59
+---
+
+# Model Card of `vocabtrimmer/mt5-small-trimmed-it-itquad-qg`
+This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-it](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [vocabtrimmer/mt5-small-trimmed-it](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it)   
+- **Language:** it  
+- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
+- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+
+### Usage
+- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
+```python
+from lmqg import TransformersQG
+
+# initialize model
+model = TransformersQG(language="it", model="vocabtrimmer/mt5-small-trimmed-it-itquad-qg")
+
+# model prediction
+questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-it-itquad-qg")
+output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   80.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_1     |   22.44 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_2     |   14.65 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_3     |   10.11 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| Bleu_4     |    7.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| METEOR     |   17.45 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| MoverScore |   56.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+| ROUGE_L    |   21.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_itquad
+ - dataset_name: default
+ - input_types: paragraph_answer
+ - output_types: question
+ - prefix_types: None
+ - model: vocabtrimmer/mt5-small-trimmed-it
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 14
+ - batch: 32
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 2
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-itquad-qg/raw/main/trainer_config.json).
+
+## Citation
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+
+```

added_tokens.json

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+}

config.json

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+  "add_prefix": false,
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
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+  "d_kv": 64,
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+  "dense_act_fn": "gelu_new",
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+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
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+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
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+  "num_heads": 6,
+  "num_layers": 8,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "use_cache": true,
+  "vocab_size": 111057,
+  "vocabtrimmer": {
+    "mining_config": {
+      "dataset": "vocabtrimmer/mc4_validation",
+      "dataset_column": "text",
+      "dataset_name": "it",
+      "dataset_split": "validation",
+      "language": "it",
+      "min_frequency": 2,
+      "target_vocab_size": null
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+      "parameter_size_embedding/trimmed": 113721344,
+      "parameter_size_full/raw": 300176768,
+      "parameter_size_full/trimmed": 157783424,
+      "vocab_size/raw": 250112,
+      "vocab_size/trimmed": 111056
+    }
+  }
+}

eval/metric.first.answer.paragraph_answer.question.lmqg_qg_itquad.default.json

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eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json

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generation_config.json

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+  "transformers_version": "4.26.1"
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special_tokens_map.json

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trainer_config.json

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