Large model

README.md

@@ -1,3 +1,113 @@
 ---
+tags:
+- text2text-generation
+- definition-modeling
+metrics:
+- rouge, bleu, bert-f1
+model-index:
+- name: flan-t5-definition-en-large
+  results: []
+language:
+- en
+widget:
+- text: "He ate a sweet apple. What is the definition of apple?"
+  example_title: "Definition generation"
+- text: "The paper contains a number of original ideas about color perception. What is the definition of original?"
+  example_title: "Definition generation"
 license: cc-by-sa-4.0
+datasets:
+- marksverdhei/wordnet-definitions-en-2021
 ---
+
+
+# FLAN-T5-Definition Large
+
+This model is a version of [FLAN-T5 Large](https://huggingface.co/google/flan-t5-large) finetuned on a dataset of English definitions and usage examples.
+
+It generates definitions of English words in context.
+Its input is the usage example and the instruction question "What is the definiton of TARGET_WORD?"
+
+This project is a collaboration between the [Dialogue Modelling Group](https://dmg-illc.github.io/dmg/) at the University of Amsterdam 
+and the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) at the University of Oslo.
+
+## Sizes:
+- [FLAN-T5-Definition Base (250M parameters)](https://huggingface.co/ltg/flan-t5-definition-en-base)
+- [FLAN-T5-Definition Large (780M parameters)](https://huggingface.co/ltg/flan-t5-definition-en-large)
+- [FLAN-T5-Definition XL (3B parameters)](https://huggingface.co/ltg/flan-t5-definition-en-xl)
+
+## Model description
+
+See details in the paper `Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis` (ACL'2023) by Mario Giulianelli, Iris Luden, Raquel Fernandez and Andrey Kutuzov.
+
+## Intended uses & limitations
+
+The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions.
+
+The fine-tuning datasets were limited to English.
+Although the original FLAN-T5 is a multilingual model, we did not thoroughly evaluate its ability to generate definitions in languages other than English. 
+
+Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model.
+
+## Training and evaluation data
+
+Three datasets were used to fine-tune the model:
+- *WordNet* ([Ishiwatari et al., NAACL 2019](https://aclanthology.org/N19-1350/)), also [available on HF](https://huggingface.co/datasets/marksverdhei/wordnet-definitions-en-2021)
+- *Oxford dictionary or CHA* ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/))
+- English subset of *CodWoE* ([Mickus et al., SemEval 2022](https://aclanthology.org/2022.semeval-1.1/))
+
+FLAN-T5-Definition Large achieves the following results on the WordNet test set:
+- BLEU: 14.37
+- ROUGE-L: 33.74
+- BERT-F1: 88.21
+
+FLAN-T5-Definition Large achieves the following results on the Oxford dictionary test set:
+- BLEU: 10.90
+- ROUGE-L: 30.05
+- BERT-F1: 87.44
+
+## Training procedure
+
+FLAN-T5 Base was fine-tuned in a sequence-to-sequence mode on examples of contextualized dictionary definitions.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 64
+- total_eval_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15.0
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 2.1769        | 1.0   | 2740  | 1.9050          | 28.7222 | 9.1873  | 26.6888 | 26.6937   | 11.3429 |
+| 1.9408        | 2.0   | 5480  | 1.8151          | 29.8799 | 10.2327 | 27.7947 | 27.8044   | 11.4165 |
+| 1.8124        | 3.0   | 8220  | 1.7608          | 30.9845 | 10.9982 | 28.8059 | 28.8131   | 11.5310 |
+| 1.7118        | 4.0   | 10960 | 1.7229          | 31.6943 | 11.7412 | 29.4967 | 29.5319   | 11.7037 |
+| 1.6286        | 5.0   | 13700 | 1.6937          | 32.5839 | 12.2431 | 30.1799 | 30.206    | 11.7784 |
+| 1.5597        | 6.0   | 16440 | 1.6748          | 32.9915 | 12.8514 | 30.7016 | 30.7145   | 11.5974 |
+| 1.4982        | 7.0   | 19180 | 1.6578          | 33.2157 | 13.1389 | 30.9428 | 30.9519   | 11.3580 |
+| 1.4468        | 8.0   | 21920 | 1.6473          | 33.6146 | 13.5922 | 31.3001 | 31.3235   | 11.5724 |
+| 1.4022        | 9.0   | 24660 | 1.6384          | 34.1711 | 14.1117 | 31.7951 | 31.8066   | 11.7389 |
+| 1.364         | 10.0  | 27400 | 1.6337          | 34.5489 | 14.5012 | 32.1329 | 32.1446   | 11.6659 |
+| 1.3321        | 11.0  | 30140 | 1.6291          | 34.7133 | 14.7297 | 32.3042 | 32.314    | 11.8003 |
+| 1.3054        | 12.0  | 32880 | 1.6267          | 34.9411 | 15.0282 | 32.5335 | 32.5451   | 11.7619 |
+| 1.2845        | 13.0  | 35620 | 1.6262          | 35.1648 | 15.2154 | 32.7387 | 32.742    | 11.8317 |
+| 1.2699        | 14.0  | 38360 | 1.6257          | 35.2849 | 15.3109 | 32.8508 | 32.853    | 11.8168 |
+| 1.2595        | 15.0  | 41100 | 1.6273          | 35.2224 | 15.2781 | 32.7718 | 32.7826   | 11.7971 |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+rocm5.1.1
+- Datasets 2.4.0
+- Tokenizers 0.12.1

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training_args.bin

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