Add models files for English model

README.md

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 license: cc-by-4.0
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+
+
+## Prompt-based learning for Lexical Simplification: prompt-ls-en-1 
+
+We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
+This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/) 
+by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
+You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
+
+## Models
+
+Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
+
+| Model Name                                                           | Run # |  Language   | Setting       |
+|----------------------------------------------------------------------|-------|:-----------:|---------------|
+| **[prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1)** | **1** | **English** | **fine-tune** |
+| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2)     | 2     |   English   | fine-tune     |
+| [roberta-large](https://huggingface.co/roberta-large)                | 3     |   English   | zero-shot     |
+| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1)     | 1     |   Spanish   | fine-tune     |
+| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2)     | 2     |   Spanish   | fine-tune     |
+| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3)     | 3     |   Spanish   | fine-tune     |
+| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1)     | 1     | Portuguese  | fine-tune     |
+| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2)     | 2     | Portuguese  | fine-tune     |
+| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3)     | 3     | Portuguese  | fine-tune     |
+
+
+For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
+
+## Results
+
+We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in development, which show an increased performance for Spanish and Portuguese. 
+You can find more details in our [paper]().
+
+| Language   | # | Model | Setting      | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3         |
+|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
+| English    | 1 | RoBERTa-L | fine-tune    | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739**  |
+| English    | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 |  0.4235 | 0.3273 | 0.7560 |
+| English    | 3 | RoBERTa-L | zero-shot    | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
+| Spanish    | 1 | BERTIN | fine-tune    | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
+| Spanish    | 2 | BERTIN | fine-tune    | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
+| Spanish    | 3 | BERTIN | fine-tune    | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 |  0.2128 | 0.5326 |
+| Portuguese | 1 | BR_BERTo | fine-tune    | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
+| Portuguese | 2 | BR_BERTo | fine-tune    | sinônimo | fácil | - | 10 | 0.1363  | 0.0962 | 0.0944 | 0.2379 |
+| Portuguese | 3 | BR_BERTo | fine-tune    | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
+
+
+## Citation
+
+If you use our results and scripts in your research, please cite our work: "[UoM&MMU at TSAR-2022 Shared Task]()" (to be published) 
+
+```
+@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
+    title = "UoM|MMU at TSAR-2022 Shared Task",
+    author = "V{|'a}squez-Rodr{|'|i}guez, Laura  and
+      Nguyen, Nhung T. H. and
+      Shardlow, Matthew and
+      Ananiadou, Sophia",
+    booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
+    month = dec,
+    year = "2022",
+}
+```

config.json

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+{
+  "_name_or_path": "bert-base-multilingual-uncased",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

pytorch_model.bin

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+oid sha256:e69d24a7077d49a6ef7d840c5a9e47a35866c80c57ed9c6a37317a136bc9ff12
+size 669926955

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-multilingual-uncased", "tokenizer_class": "BertTokenizer"}