Model

README.md

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 ---
+tags:
+- text2text-generation
+- definition-modeling
+metrics:
+- rouge, bleu, bert-f1
+model-index:
+- name: flan-t5-definition-en-xl
+  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: "Definiton generation"
 license: cc-by-sa-4.0
+datasets:
+- wordnet-definitions-en-2021
 ---
+
+# FLAN-T5-Definition XL
+
+This model is a version of [FLAN-T5 XL](https://huggingface.co/google/flan-t5-xl) 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?"
+
+## 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 XL achieves the following results on the WordNet test set ([Ishiwatari et al., NAACL 2019](https://aclanthology.org/N19-1350/)):
+- ROUGE-L: 52.21
+- BLEU: 32.81
+- BERT-F1: 92.16
+
+FLAN-T5-Definition XL achieves the following results on the Oxford dictionary test set ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/)):
+- ROUGE-L: 38.72
+- BLEU: 18.69
+- BERT-F1: 89.75
+
+## Training procedure
+FLAN-T5 XL 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: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 16
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20.0
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+rocm5.1.1
+- Datasets 2.4.0
+- Tokenizers 0.12.1
+
+## Citation

config.json

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+{
+  "_name_or_path": "../../models/flan-t5-xl/",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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