diff --git a/README.md b/README.md
index 509dab039454f5d885b6228157b663c38c8a98d5..cb84f31c0bb94b672ff5967f553567925a6dcf78 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,39 @@
-# ner_workflows
+# Named entity recognition on Workflow data
+## Description
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+This directory contains all the necessary information and scripts to reproduce the results presented in :
```
-cd existing_repo
-git remote add origin https://gitlab.liris.cnrs.fr/sharefair/ner_workflows.git
-git branch -M main
-git push -uf origin main
+@misc{sebe2024extractinginformationlowresourcesetting,
+ title={Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows},
+ author={Clémence Sebe and Sarah Cohen-Boulakia and Olivier Ferret and Aurélie Névéol},
+ year={2024},
+ eprint={2411.19295},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2411.19295},
+}
```
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.liris.cnrs.fr/sharefair/ner_workflows/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+## Before You Start
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+Before running the experiments, you need to:
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+* Download the dataset : https://doi.org/10.5281/zenodo.14879025
+* Clone the Git repository : https://github.com/ClemenceS/nlstruct
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+## Contents
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+This repository includes:
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+* A python script, `run_nlstruct.py`, to launch NER experiences whose header information must be modified (data link and model to be trained)
+* A jupyter notebook, `add_voc_bioinfo.ipynb`, to integrate bioinformatics tools and binaries into models.
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+## Licence
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+This project is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
-## License
-For open source projects, say how it is licensed.
+## Funding
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+This work received support from the National Research Agency under the France 2030 program, with reference to ANR-22-PESN-0007.
\ No newline at end of file
diff --git a/add_voc_bioinfo.ipynb b/add_voc_bioinfo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9d8fcf19a7ededa097534ae1d733423819f75818
--- /dev/null
+++ b/add_voc_bioinfo.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "cc36391f-a1b4-4958-9e2d-0034cad07759",
+ "metadata": {},
+ "source": [
+ "# Add vocabulary in a nlp model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e53dd5bf-2eff-426d-aa38-f5ac3e812a53",
+ "metadata": {},
+ "source": [
+ "Clémence SEBE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2045ee21-bf1e-4da4-9514-07d7ee270a7b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import re\n",
+ "import json\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from transformers import AutoTokenizer, AutoModel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae0a823a-f289-43b6-af29-71ca821fe6db",
+ "metadata": {},
+ "source": [
+ "I reuse fonctions developped in : https://github.com/LeonidasY/fast-vocabulary-transfer "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca110132-dc16-46ba-adf6-edd978c5bbbb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def tokens_mapping(in_tokenizer, gen_tokenizer):\n",
+ " \"\"\"\n",
+ " https://github.com/LeonidasY/fast-vocabulary-transfer/blob/main/fvt/fvt.py\n",
+ " \n",
+ " This method establish a mapping between each token of\n",
+ " the in-domain tokenizer (in_tokenizer) to one or more tokens from\n",
+ " the general-purpose (gen_tokenizer) tokenizer.\n",
+ "\n",
+ " :param in_tokenizer: Any huggingface tokenizer\n",
+ " :param gen_tokenizer: Any huggingface tokenizer\n",
+ " :param kwargs: no kwargs\n",
+ "\n",
+ " :return: A dictionary, having size of the in_tokenizer vocabulary.\n",
+ " Each key is the index corresponding to a token in the in-tokenizer.\n",
+ " Values are lists of indexes to the tokens of gen_tokenizer.\n",
+ " \"\"\"\n",
+ "\n",
+ " gen_vocab = gen_tokenizer.get_vocab()\n",
+ " in_vocab = in_tokenizer.get_vocab()\n",
+ " ngram_vocab = in_tokenizer.ngram_vocab if hasattr(in_tokenizer, 'ngram_vocab') else {}\n",
+ "\n",
+ " tokens_map = {}\n",
+ " for new_token, new_index in in_vocab.items():\n",
+ " if new_token in gen_vocab:\n",
+ " # if the same token exists in the old vocabulary, take its embedding\n",
+ " old_index = gen_vocab[new_token]\n",
+ " tokens_map[new_index] = [old_index]\n",
+ " \n",
+ " else:\n",
+ " # if not, tokenize the new token using the old vocabulary\n",
+ " new_token = re.sub('^(##|Ä |â–)', '', new_token)\n",
+ " if new_token in ngram_vocab:\n",
+ " token_partition = gen_tokenizer.tokenize(new_token.split('‗'), is_split_into_words=True)\n",
+ " else:\n",
+ " token_partition = gen_tokenizer.tokenize(new_token)\n",
+ "\n",
+ " tokens_map[new_index] = [gen_vocab[old_token] for old_token in token_partition]\n",
+ "\n",
+ " return tokens_map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "775e5207-2f6c-4603-aaec-dbd3fe2eb614",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def embeddings_assignment(tokens_map, gen_model):\n",
+ " \"\"\"\n",
+ " https://github.com/LeonidasY/fast-vocabulary-transfer/blob/main/fvt/fvt.py\n",
+ " \n",
+ " Given a mapping between two tokenizers and a general-purpose model\n",
+ " trained on gen_tokenizer, this method produces a new embedding matrix\n",
+ " assigning embeddings from the gen_model.\n",
+ "\n",
+ " :param tokens_map: A mapping between new and old tokens. See tokens_mapping(...)\n",
+ " :param gen_model: A huggingface model, e.g. bert\n",
+ " :param kwargs: no kwargs\n",
+ "\n",
+ " :return: (2-d torch.Tensor) An embedding matrix with same size of tokens_map.\n",
+ " \"\"\"\n",
+ "\n",
+ " gen_matrix = gen_model.get_input_embeddings().weight\n",
+ " in_matrix = torch.zeros(len(tokens_map), gen_matrix.shape[1])\n",
+ "\n",
+ " for new_index, old_indices in tokens_map.items():\n",
+ " old_embedding = torch.mean(gen_matrix[old_indices], axis=0)\n",
+ " in_matrix[new_index] = old_embedding\n",
+ "\n",
+ " return in_matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14d8ed15-805f-4096-b049-cfdbe21bd81f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def update_model_embeddings(gen_model, in_matrix, **kwargs):\n",
+ " \"\"\"\n",
+ " https://github.com/LeonidasY/fast-vocabulary-transfer/blob/main/fvt/__init__.py\n",
+ " \n",
+ " Method that replaces the embeddings of a given LM with in_matrix.\n",
+ "\n",
+ " :param gen_model: An huggingface model, e.g. bert\n",
+ " :param in_matrix: (2-d torch.Tensor) The new embedding matrix.\n",
+ " :param kwargs: no kwargs\n",
+ "\n",
+ " :return: A new LM model with replaced embeddings\n",
+ " \"\"\"\n",
+ "\n",
+ " # Change the model's embedding matrix\n",
+ " gen_model.get_input_embeddings().weight = nn.Parameter(in_matrix)\n",
+ " gen_model.config.vocab_size = in_matrix.shape[0]\n",
+ "\n",
+ " tie_weights = kwargs.get('tie_weights', True)\n",
+ " if tie_weights:\n",
+ " # Tie the model's weights\n",
+ " gen_model.tie_weights()\n",
+ "\n",
+ " return gen_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "524f7e07-725a-4f0b-b529-ed2f12108202",
+ "metadata": {},
+ "source": [
+ "### First part: list of tool names and binaries to be added"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de4b1ae4-466f-4a4f-90a0-4a1c047daab3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "which_file = \"alias_bin_list.json\"\n",
+ "with open(which_file, 'r') as mon_fichier:\n",
+ " json_voc = json.load(mon_fichier)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86d1d37b-fe80-4783-ba9d-278a3f994092",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list_voc_to_add = []\n",
+ "taille_tools = {}\n",
+ "for sub_tool in json_voc:\n",
+ " for tool in sub_tool: \n",
+ "\n",
+ " \n",
+ " if len(tool) >= 5 and not \"githubusercontent\" in tool:\n",
+ " temp_tool = tool.split(\".\")[0]\n",
+ " temp_tool = temp_tool.split(\":\")[0]\n",
+ " temp_tool = ' '.join(re.sub(r'^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$', '', mot) for mot in temp_tool.split())\n",
+ " if len(temp_tool) >= 4 and len(temp_tool) < 30:\n",
+ " list_voc_to_add.append(temp_tool)\n",
+ " if not len(temp_tool) in taille_tools:\n",
+ " taille_tools[len(temp_tool)] = 1\n",
+ " else:\n",
+ " taille_tools[len(temp_tool)] += 1\n",
+ " \"\"\"\n",
+ " if len(tool) > 3 and not \"githubusercontent\" in tool:\n",
+ " list_voc_to_add.append(tool)\n",
+ " \"\"\" \n",
+ " \n",
+ "list_voc_to_add = list(set(list_voc_to_add))\n",
+ "print(f\"{len(list_voc_to_add)} peuvent être ajouté dans les modèles de langues\")\n",
+ "\n",
+ "file = open(\"list_voc.txt\", 'w')\n",
+ "for t in list_voc_to_add:\n",
+ " file.write(f\"{t}\\n\")\n",
+ "file.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d63713bc-2352-4848-97bf-937ccbfd7d09",
+ "metadata": {},
+ "source": [
+ "# All in once"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a22a39ad-8c50-4001-9462-894b8bfe7592",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_name = [\"bert-base-uncased\", \"allenai/scibert_scivocab_uncased\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb05f7f2-210d-4a86-b9d0-e6d49aa24f24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for model_to_do in model_name:\n",
+ " print(f\"\\t{model_to_do}\")\n",
+ " \n",
+ " tokenizer_model_to_do = AutoTokenizer.from_pretrained(model_to_do)\n",
+ " avant = len(tokenizer_model_to_do)\n",
+ " print(f\"Len tokenizer avant ajout : {len(tokenizer_model_to_do)}\")\n",
+ " tokenizer_model_to_do.add_tokens(list_voc_to_add)\n",
+ " print(f\"Len tokenizer après ajout : {len(tokenizer_model_to_do)}\")\n",
+ " print(f'Nb voc add : {len(tokenizer_model_to_do) - avant}')\n",
+ "\n",
+ " # resize token embedding\n",
+ " model_work_on = AutoModel.from_pretrained(model_to_do)\n",
+ " model_work_on.resize_token_embeddings(len(tokenizer_model_to_do))\n",
+ "\n",
+ " #mapping between each token of the in-domain tokenizer (in_tokenizer) to one or more tokens from the general-purpose (gen_tokenizer) tokenizer.\n",
+ " tokenizer_base = AutoTokenizer.from_pretrained(model_to_do)\n",
+ " tokens_map = tokens_mapping(tokenizer_model_to_do, tokenizer_base)\n",
+ "\n",
+ " #Given a mapping between two tokenizers and a general-purpose model trained on gen_tokenizer\n",
+ " in_matrix = embeddings_assignment(tokens_map, model_work_on)\n",
+ "\n",
+ " #Method that replaces the embeddings of a given LM with in_matrix.\n",
+ " in_model = update_model_embeddings(model_work_on, in_matrix)\n",
+ "\n",
+ " assert in_model.get_input_embeddings().weight.size(0) == len(tokenizer_model_to_do), \"Error: embedding size does not match tokenizer size.\"\n",
+ "\n",
+ "\n",
+ " fold_save = \"modele_\" + model_to_do.replace(\"/\", \"_\") \n",
+ " os.makedirs(fold_save, exist_ok=True)\n",
+ "\n",
+ " for param in in_model.parameters():\n",
+ " if not param.is_contiguous():\n",
+ " param.data = param.data.contiguous()\n",
+ " #save model\n",
+ " in_model.save_pretrained(fold_save)\n",
+ " #save tokenizer\n",
+ " tokenizer_model_to_do.save_pretrained(fold_save)\n",
+ "\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6837f1a0-0c98-43f8-8407-778ad078462c",
+ "metadata": {},
+ "source": [
+ "## Check that changes have been made"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2bc6896-410f-4300-b462-6d4fa9bbfee2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "verif_poids = True\n",
+ "verif_vocab = True\n",
+ "\n",
+ "for model_to_do in model_name:\n",
+ " print(f\"\\t{model_to_do}\")\n",
+ " model_dir = \"modele_\" + model_to_do.replace(\"/\", \"_\") \n",
+ " tokenizer = AutoTokenizer.from_pretrained(model_dir)\n",
+ " model = AutoModel.from_pretrained(model_dir)\n",
+ "\n",
+ " if verif_vocab :\n",
+ " print(f\"- Check if adding voc went well (if nothing afterwards ok)\")\n",
+ " for token in list_voc_to_add:\n",
+ " if token in tokenizer.get_vocab():\n",
+ " \n",
+ " # weight verification\n",
+ " if verif_poids :\n",
+ " token_id = tokenizer.convert_tokens_to_ids(token)\n",
+ " embedding_nouveau_token = model.get_input_embeddings().weight[token_id]\n",
+ " \n",
+ " sous_tokens = tokenizer.tokenize(token, add_special_tokens=False)\n",
+ " sous_token_ids = tokenizer.convert_tokens_to_ids(sous_tokens)\n",
+ " sous_embeddings = model.get_input_embeddings().weight[sous_token_ids]\n",
+ " moyenne_sous_embeddings = torch.mean(sous_embeddings, dim=0)\n",
+ " \n",
+ " if torch.allclose(embedding_nouveau_token, moyenne_sous_embeddings, atol=1e-6):\n",
+ " pass\n",
+ " else:\n",
+ " print(f\"The embedding of the new token does not match the average of the sub-token : {token}\")\n",
+ " else:\n",
+ " print(f\"Token '{token}' is absent from the vocabulary.\")\n",
+ " \n",
+ "\n",
+ " print(f\"- Vérifier dimension du voc : \")\n",
+ " model_base = AutoModel.from_pretrained(model_to_do) \n",
+ " print(\"Base model vocabulary dimension :\", model_base.get_input_embeddings().weight.size(0))\n",
+ " print(\"Modified model vocabulary dimensions :\", model.get_input_embeddings().weight.size(0))\n",
+ "\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1e78860-5788-4f6f-8e8e-8edb2dd4d73a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/alias_bin_list.json b/alias_bin_list.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0255fb30521e4b3d5d1d4aaccdc1940eb135717
--- /dev/null
+++ b/alias_bin_list.json
@@ -0,0 +1,9476 @@
+[
+ [
+ "abacas",
+ "abacas-examples"
+ ],
+ [
+ "python3-pyabpoa",
+ "abpoa"
+ ],
+ [
+ "abricate-get_db",
+ "abricate"
+ ],
+ [
+ "abyss-sealer",
+ "abyss-tofastq",
+ "abyss-bloom-dist.mk",
+ "abyss-fixmate-ssq",
+ "abyss-bowtie",
+ "distanceest-ssq",
+ "abyss-mergepairs",
+ "logcounter",
+ "abyss-pe",
+ "abyss-todot",
+ "abyss-samtoafg",
+ "abyss-map-ssq",
+ "distanceest",
+ "abyss-paired-dbg-mpi",
+ "simplegraph",
+ "abyss-filtergraph",
+ "kaligner",
+ "abyss-bwasw",
+ "abyss-db-txt",
+ "abyss-bowtie2",
+ "pathconsensus",
+ "abyss-gc",
+ "abyss-bwamem",
+ "konnector",
+ "pathoverlap",
+ "abyss-bloom",
+ "abyss-junction",
+ "popbubbles",
+ "abyss",
+ "abyss-tabtomd",
+ "abyss-index",
+ "abyss-dida",
+ "dassembler",
+ "adjlist",
+ "abyss-longseqdist",
+ "abyss-map",
+ "abyss-paired-dbg",
+ "abyss-layout",
+ "parsealigns",
+ "mergecontigs",
+ "abyss-fatoagp",
+ "abyss-align",
+ "abyss-p",
+ "abyss-scaffold",
+ "abyss-bwa",
+ "abyss-fixmate",
+ "overlap",
+ "consensus",
+ "abyss-kaligner",
+ "mergepaths",
+ "abyss-overlap",
+ "abyss-bloom-dbg",
+ "abyss-fac",
+ "abyss-gapfill"
+ ],
+ [
+ "adapterremoval",
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+ ],
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+ "indexdb_rna",
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+ "cds-mapping-stats",
+ "bwa-spades",
+ "spades-core",
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+ "metaviralspades.py",
+ "spades-corrector-core",
+ "spades.py",
+ "coronaspades.py",
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+ "spades-truseq-scfcorrection",
+ "spades-ionhammer",
+ "metaspades",
+ "spades-kmercount",
+ "spades-hammer",
+ "mag-improve",
+ "rnaviralspades.py",
+ "truspades",
+ "cds-subgraphs",
+ "spades-read-filter",
+ "rnaspades.py",
+ "metaspades.py",
+ "plasmidspades.py",
+ "dipspades",
+ "spades-gmapper",
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+ "dipspades.py",
+ "spades-bwa",
+ "spades-gsimplifier",
+ "spaligner",
+ "truspades.py",
+ "rnaspades",
+ "spades-kmer-estimating",
+ "spades"
+ ],
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+ "spspaln.pl",
+ "makmdm",
+ "makeidx.pl",
+ "catchr.pl",
+ "sortgrcd",
+ "makdbs",
+ "spaln-data",
+ "spaln"
+ ],
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+ "optsparkbin",
+ "spark"
+ ],
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+ "randomjunctionfilter",
+ "wig2barwig",
+ "bowtie-",
+ "bowtie-build",
+ "neighborfilter",
+ "findnoveljunctions",
+ "precipitatesam",
+ "runsplicemap",
+ "amalgamatesam",
+ "subseq",
+ "sortsam",
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+ "nnrfilter",
+ "splicemap",
+ "bowtie",
+ "colorjunction",
+ "countsam",
+ "barloader"
+ ],
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+ "spp",
+ "rspp"
+ ],
+ [
+ "spreadphy",
+ "spread-phy"
+ ],
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+ "spring",
+ "rspring"
+ ],
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+ "python3-sqt",
+ "sqt"
+ ],
+ [
+ "libsquizz",
+ "squizz",
+ "libsquizz-dev"
+ ],
+ [
+ "get_genus_vfdb",
+ "csv_to_gene_db",
+ "getmlst.py",
+ "vfdb_cdhit_to_csv",
+ "srst2",
+ "get_all_vfdb",
+ "slurm_srst2",
+ "getmlst",
+ "vfdbgenus"
+ ],
+ [
+ "ssaha2",
+ "ssahasnp",
+ "ssaha2build"
+ ],
+ [
+ "makefastafilefromscaffolds.pl2",
+ "ssake",
+ "tqsfastq.py",
+ "ssake-examples",
+ "nlength.pl2",
+ "analyzepositionssake.pl2",
+ "tqsfastq.pl2",
+ "tqs.py",
+ "ssake2",
+ "makepairedoutput2unequalfiles.pl2",
+ "qseq2fastq.pl2",
+ "splitinput.pl2",
+ "getstats.pl2",
+ "tqs",
+ "qseq2fasta.pl2",
+ "makepairedoutput2equalfiles.pl2",
+ "tqsexport.py",
+ "makepairedoutput.pl2"
+ ],
+ [
+ "stacksweb",
+ "populations",
+ "phasedstacks",
+ "sstacks",
+ "ustacks",
+ "stacks_summary.py",
+ "stacks-web",
+ "stackssummary",
+ "convert_stacks.pl",
+ "process_radtags",
+ "clone_filter",
+ "stacks-dist-extract",
+ "process_shortreads",
+ "stacks-integrate-alignments",
+ "cstacks",
+ "extract_interpop_chars.pl",
+ "gstacks",
+ "denovo_map.pl",
+ "tsv2bam",
+ "stacks-count-reads-per-sample-per-locus",
+ "ref_map.pl",
+ "stacks",
+ "stacks-samtools-tview",
+ "kmer_filter",
+ "stacks-hist2d-loci-samples-coverage"
+ ],
+ [
+ "pregap4",
+ "gap4",
+ "gap5",
+ "staden",
+ "staden-common",
+ "trev"
+ ],
+ [
+ "staphopia-sccmec",
+ "staphopia",
+ "staphopiasccmec"
+ ],
+ [
+ "rna-star",
+ "starlong",
+ "star",
+ "rnastar"
+ ],
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+ "staramr.blast.results.pointfinder",
+ "staramr.exceptions",
+ "staramr.tests.unit",
+ "staramr.subcommand",
+ "staramr.blast.pointfinder",
+ "staramr.tests.integration.databases",
+ "staramr.blast.resfinder",
+ "staramr.tests",
+ "staramr.tests.integration.detection",
+ "staramr.tests.unit.blast.results.pointfinder.codon",
+ "staramr.tests.integration",
+ "staramr.tests.unit.blast.results.pointfinder",
+ "staramr.detection",
+ "staramr.blast.results",
+ "staramr.tests.unit.blast.results.pointfinder.nucleotide",
+ "staramr.tests.unit.results",
+ "staramr.blast.results.pointfinder.codon",
+ "staramr.databases.exclude",
+ "staramr.databases.resistance.pointfinder",
+ "staramr.tests.unit.blast",
+ "staramr.databases.resistance",
+ "staramr.databases.resistance.resfinder",
+ "staramr.blast.results.resfinder",
+ "staramrdb",
+ "staramr.databases",
+ "staramrsearch",
+ "staramr",
+ "staramr.blast",
+ "staramr.results",
+ "staramr.tests.unit.blast.pointfinder",
+ "staramr.blast.results.pointfinder.nucleotide",
+ "staramr.tests.unit.blast.results"
+ ],
+ [
+ "starfusion",
+ "prep_genome_lib.pl",
+ "blast_and_promiscuity_filter.pl",
+ "star-fusion"
+ ],
+ [
+ "startasap",
+ "start-asap"
+ ],
+ [
+ "configurestrelkagermlineworkflow.py",
+ "configurestrelkasomaticworkflow.py",
+ "strelka"
+ ],
+ [
+ "prepde.py",
+ "stringtie"
+ ],
+ [
+ "structure-gui",
+ "structure"
+ ],
+ [
+ "featurecounts",
+ "flattengtf",
+ "subread-data",
+ "subread-fullscan",
+ "subjunc",
+ "exactsnp",
+ "qualityscores",
+ "subreaddata",
+ "repair",
+ "sublong",
+ "subread-align",
+ "subindel",
+ "removedup",
+ "detectioncall",
+ "txunique",
+ "genrandomreads",
+ "subread",
+ "propmapped",
+ "subread-buildindex"
+ ],
+ [
+ "python3-dendropy",
+ "python-dendropy",
+ "sumtrees"
+ ],
+ [
+ "update_info.py",
+ "vcf_group_multiline.py",
+ "vcf_modify_header.py",
+ "svtyper",
+ "sv_counts.sh",
+ "svtyper-sso",
+ "batchtyper",
+ "sv_classifier.py",
+ "vcf_paste.py",
+ "vcf_allele_freq.py"
+ ],
+ [
+ "graph_plot.py",
+ "swarm"
+ ],
+ [
+ "sweed",
+ "sweed-p"
+ ],
+ [
+ "t-coffee",
+ "tcoffee",
+ "hometmpt_coffee",
+ "t-coffee-examples"
+ ],
+ [
+ "htslibtest",
+ "tabix",
+ "libhts1",
+ "testtest1.bed",
+ "bgziptest1.bed",
+ "bgzip",
+ "htslib-test",
+ "libhts-private-dev",
+ "htsfile",
+ "libhts-dev",
+ "libhts3t64",
+ "libhts2",
+ "libhts3",
+ "htsfiletest1.bed.gz",
+ "pytabix",
+ "tabix.py",
+ "tabixtest1.bed.gz",
+ "htslib"
+ ],
+ [
+ "taxatortk",
+ "alignments-filter",
+ "binner",
+ "fasta-strip-identifier",
+ "lastmaf2alignments",
+ "taxsummary2krona",
+ "taxator",
+ "lastmaf2alignments-parallel",
+ "ktimporttext",
+ "taxknife"
+ ],
+ [
+ "libteem2",
+ "libteem2-dbg",
+ "libteem-dev",
+ "teem-apps"
+ ],
+ [
+ "terraphast",
+ "libterraces0",
+ "libterraces-dev"
+ ],
+ [
+ "texlive-fonts-extra",
+ "texlive-math-extra",
+ "texlive-plain-generic",
+ "texlive-extra-utils",
+ "texlive-plain-extra",
+ "texlive-pstricks",
+ "texlive-humanities",
+ "texlive-fonts-extra-links",
+ "texlive-formats-extra",
+ "texpower",
+ "texlive-music",
+ "etoolbox",
+ "texlive-bibtex-extra",
+ "texlive-publishers",
+ "texlive-latex3",
+ "texlive-generic-extra",
+ "texlive-latex-extra",
+ "pdfjam",
+ "texlive-humanities-doc",
+ "texlive-htmlxml",
+ "texlive-pstricks-doc",
+ "texlive-fonts-extra-doc",
+ "texlive-latex-extra-doc",
+ "texlive-publishers-doc",
+ "texlive-science-doc",
+ "texlive-science",
+ "texlive-games",
+ "texlive-font-utils"
+ ],
+ [
+ "r-bioc-tfbstools",
+ "tfbstools"
+ ],
+ [
+ "theseus_align",
+ "theseus-examples",
+ "theseus"
+ ],
+ [
+ "fermi2",
+ "tiddit",
+ "ropebwt2",
+ "bwa"
+ ],
+ [
+ "python3-tifffile",
+ "tifffile",
+ "python-tifffile",
+ "lsm2bin"
+ ],
+ [
+ "long-orfs",
+ "tigrglimmer",
+ "test",
+ "build-icm",
+ "score-fixed",
+ "g3-from-training.csh",
+ "g3-iterated.csh",
+ "build-fixed",
+ "tigr-run-glimmer3",
+ "match-list-col.awk",
+ "anomaly",
+ "start-codon-distrib",
+ "not-acgt.awk",
+ "window-acgt",
+ "entropy-profile",
+ "tigr-glimmer",
+ "entropy-score",
+ "uncovered",
+ "glimmer3",
+ "glim-diff.awk",
+ "g3-from-scratch.csh",
+ "upstream-coords.awk",
+ "extract",
+ "glimmer",
+ "multi-extract",
+ "get-motif-counts.awk"
+ ],
+ [
+ "python3-tinyalign",
+ "tinyalign"
+ ],
+ [
+ "titancna",
+ "r-bioc-titancna"
+ ],
+ [
+ "tmscore",
+ "tmalign"
+ ],
+ [
+ "_toil_worker",
+ "toil",
+ "toil.wdl",
+ "toil.server.wes",
+ "toil.server.cli",
+ "toil.provisioners",
+ "toil.jobstores",
+ "toil.jobstores.aws",
+ "toil.utils",
+ "toil.cwl",
+ "toil.test",
+ "toil.server.api_spec",
+ "toil.lib.encryption",
+ "toil-cwl-runner",
+ "toil.provisioners.aws",
+ "toil.batchsystems",
+ "toil.lib",
+ "toil-wdl-runner",
+ "toil.server",
+ "toil.filestores"
+ ],
+ [
+ "tombo",
+ "onttombo",
+ "tombo-doc"
+ ],
+ [
+ "tophat-fusion-post",
+ "fix_map_ordering",
+ "gtf_juncs",
+ "map2gtf",
+ "prep_reads",
+ "sam_juncs",
+ "sra_to_solid",
+ "tophat_reports",
+ "bam2fastx",
+ "segment_juncs",
+ "tophat2",
+ "juncs_db",
+ "tophat",
+ "bam_merge",
+ "bed_to_juncs",
+ "gtf_to_fasta",
+ "long_spanning_reads",
+ "contig_to_chr_coords"
+ ],
+ [
+ "tophat-recondition",
+ "tophatrecondition"
+ ],
+ [
+ "openms-common",
+ "toppas",
+ "openms",
+ "openmsmeta",
+ "libopenms-dev",
+ "pyopenms",
+ "condalinkagesprefixpkg_name",
+ "topp",
+ "libopenms2.",
+ "condaobjectsprefixpkg_name",
+ "libopenms1.11",
+ "openms-doc"
+ ],
+ [
+ "null",
+ "tracer"
+ ],
+ [
+ "geargenomics",
+ "tracy",
+ "dicey"
+ ],
+ [
+ "traitarpfam",
+ "heatmap.py",
+ "traitarphenotype",
+ "hmm2gff.py",
+ "merge_preds.py",
+ "predict.py",
+ "traitar",
+ "hmmer2filtered_best.py",
+ "domtblout2gene_generic.py"
+ ],
+ [
+ "transabyss-merge",
+ "transabyss-analyze",
+ "transabyss"
+ ],
+ [
+ "perldbdmysql",
+ "dbdmysql"
+ ],
+ [
+ "gff3_file_to_bed.pl",
+ "transdecoder-doc",
+ "cdna_alignment_orf_to_genome_orf.pl",
+ "get_longest_orf_per_transcript.pl",
+ "seq_n_baseprobs_to_loglikelihood_vals.pl",
+ "pfam_runner.pl",
+ "feature_scoring.+-.pl",
+ "refine_gff3_group_iso_strip_utrs.pl",
+ "gtf_to_alignment_gff3.pl",
+ "make_seqlogo.rscript",
+ "compute_auc.pl",
+ "gene_list_to_gff.pl",
+ "ffindex_resume.pl",
+ "compute_base_probs.pl",
+ "gff3_file_to_proteins.pl",
+ "gtf_to_bed.pl",
+ "gff3_gene_to_gtf_format.pl",
+ "transdecoder.predict",
+ "transdec",
+ "plot_roc.rscript",
+ "deplete_feature_noise.pl",
+ "start_codon_refinement.pl",
+ "get_fl_accs.pl",
+ "transdecoder",
+ "train_start_pwm.pl",
+ "score_cds_likelihood_all_6_frames.pl",
+ "gtf_genome_to_cdna_fasta.pl",
+ "get_top_longest_fasta_entries.pl",
+ "exclude_similar_proteins.pl",
+ "feature_scores_to_roc.pl",
+ "simulate_feature_seq_from_pwm.pl",
+ "remove_eclipsed_orfs.pl",
+ "refine_hexamer_scores.pl",
+ "build_atgpwm_+-.pl",
+ "fasta_prot_checker.pl",
+ "nr_orfs_gff3.pl",
+ "select_best_orfs_per_transcript.pl",
+ "transdecoder.longorfs"
+ ],
+ [
+ "tpp",
+ "transit-tpp",
+ "tnseqtransit",
+ "tnseq-transit",
+ "transit"
+ ],
+ [
+ "bam-read",
+ "transratetools",
+ "transrate-tools"
+ ],
+ [
+ "transtermhp",
+ "2ndscore",
+ "transterm"
+ ],
+ [
+ "treecluster.py",
+ "treecluster"
+ ],
+ [
+ "threepop",
+ "fourpop",
+ "treemix",
+ "plotting_funcs.r",
+ "f4ratio"
+ ],
+ [
+ "tree-puzzle-doc",
+ "treepuzzle",
+ "tree-puzzle",
+ "tree-ppuzzle",
+ "treeppuzzle"
+ ],
+ [
+ "puzzle",
+ "treepuzzle",
+ "ppuzzle"
+ ],
+ [
+ "tree-qmc",
+ "treeqmc"
+ ],
+ [
+ "python3-treetime",
+ "treetime",
+ "timetree"
+ ],
+ [
+ "treeview",
+ "javatreeview",
+ "treeviewx"
+ ],
+ [
+ "trf-examples",
+ "trf"
+ ],
+ [
+ "trimgalore",
+ "trim_galore",
+ "trim-galore"
+ ],
+ [
+ "statal",
+ "readal",
+ "trimal"
+ ],
+ [
+ "trimmomaticse",
+ "trimmomaticpe",
+ "trimmomatic"
+ ],
+ [
+ "align_and_estimate_abundance.pl",
+ "trinity_gene_splice_modeler.py",
+ "trinityrnaseq-examples",
+ "trinitystats.pl",
+ "run_de_analysis.pl",
+ "define_clusters_by_cutting_tree.pl",
+ "trinityrnaseq",
+ "ptr",
+ "trinity",
+ "analyze_diff_expr.pl",
+ "abundance_estimates_to_matrix.pl"
+ ],
+ [
+ "eufindtrna",
+ "trnascanse",
+ "mitohighconfidencefilter",
+ "trnascan",
+ "trnascan-se-common",
+ "coves-se",
+ "sstofa",
+ "trnascan-",
+ "setup.trnascan-se",
+ "covels-se",
+ "trnascan-se",
+ "eukhighconfidencefilter",
+ "fasta2gsi"
+ ],
+ [
+ "simtr",
+ "trtools.simtr",
+ "comparestr",
+ "mergestr",
+ "trtools.prancstr",
+ "trtools.comparestr",
+ "trtools",
+ "associatr",
+ "prancstr",
+ "trtools.statstr",
+ "statstr",
+ "qcstr",
+ "trtools.qcstr",
+ "dumpstr",
+ "trtools.dumpstr",
+ "trtools.utils",
+ "trtools.associatr",
+ "trtools.mergestr"
+ ],
+ [
+ "tvcutils",
+ "tvc",
+ "variant_caller_pipeline.py",
+ "tvcassembly"
+ ],
+ [
+ "graphdump",
+ "twopaco"
+ ],
+ [
+ "r-bioc-tximport",
+ "tximport"
+ ],
+ [
+ "ucecho",
+ "uc-echo",
+ "echo"
+ ],
+ [
+ "bedgraphtobigwig",
+ "ucscbedgraphtobigwig"
+ ],
+ [
+ "ucscliftover",
+ "liftover"
+ ],
+ [
+ "ugene-data",
+ "ugene"
+ ],
+ [
+ "umis-examples",
+ "lc_alllangumis",
+ "umis",
+ "lc_allc.utf-8umis"
+ ],
+ [
+ "umitools",
+ "umi_tools"
+ ],
+ [
+ "unicycler_polish",
+ "unicycler-data",
+ "unicycler",
+ "columns80unicycler",
+ "unicycler_check",
+ "unicycler_scrub",
+ "unicycler_align"
+ ],
+ [
+ "uniprot.ws",
+ "uniprotws"
+ ],
+ [
+ "unitigcounter",
+ "unitig-counter",
+ "cdbg-ops"
+ ],
+ [
+ "varscan",
+ "varscanmpileup2cns"
+ ],
+ [
+ "vcfanno",
+ "vcfanno-examples"
+ ],
+ [
+ "vcfuniqtest.vcf",
+ "vcffirstheader<",
+ "vcfwave",
+ "vcfsort<",
+ "test.vcfhttps:raw.githubusercontent.comvcflibvcfliba610b7444daab0c9e974d9a001550bf8dd644770testdataregressionvcffilter_2.vcf",
+ "vcfstreamsort",
+ "vcflib"
+ ],
+ [
+ "fill-aa",
+ "vcftools",
+ "vcf-compare",
+ "vcf-concat",
+ "tab-to-vcf",
+ "fill-ref-md5",
+ "perlusevcf",
+ "vcf-fix-newlines",
+ "perlvcftoolsvcf",
+ "vcf-consensus",
+ "vcf-haplotypes",
+ "vcf-contrast",
+ "vcf-indel-stats",
+ "vcf-convert",
+ "vcf-fix-ploidy",
+ "fill-fs",
+ "vcf-annotate",
+ "fill-an-ac"
+ ],
+ [
+ "vcontact2_gene2genome",
+ "vcontact2"
+ ],
+ [
+ "perlvelvetoptimiser",
+ "velvetoptimiser.pl",
+ "velvetoptimiser"
+ ],
+ [
+ "vg",
+ "vg-docs"
+ ],
+ [
+ "vibrant_annotation.py",
+ "vibrant_run.py",
+ "vibrant"
+ ],
+ [
+ "vienna-rna",
+ "viennarnapackage",
+ "python3-rna",
+ "librna-perl"
+ ],
+ [
+ "viralmsa",
+ "viralmsa.py"
+ ],
+ [
+ "viralverify",
+ "training_script"
+ ],
+ [
+ "fastq_len_filter.py",
+ "viromeqc",
+ "viromeqc.py"
+ ],
+ [
+ "virsorterrun",
+ "virsortertrain-model",
+ "virsortersetup",
+ "virsortertrain-feature",
+ "virsorter",
+ "wrapper_phage_contigs_sorter_iplant.pl"
+ ],
+ [
+ "git",
+ "virulencefinder.py",
+ "virulencefinder"
+ ],
+ [
+ "vsubseqselect",
+ "vstree2tex",
+ "vmigrate.sh",
+ "mkdna6idx",
+ "vseqselect",
+ "libvmatch-dev",
+ "vendian",
+ "vmatchselect",
+ "chain2dim",
+ "repfind.pl",
+ "vseqinfo",
+ "cleanpp.sh",
+ "matchcluster",
+ "mkvtree",
+ "vmatch"
+ ],
+ [
+ "libvmtk1.0",
+ "python-vmtk",
+ "libvmtk-dev",
+ "libvmtk1.3",
+ "vmtk"
+ ],
+ [
+ "vsclust",
+ "vsclust.yml"
+ ],
+ [
+ "vsearch-examples",
+ "vsearch"
+ ],
+ [
+ "vt-examples",
+ "vt"
+ ],
+ [
+ "weeder2",
+ "weeder"
+ ],
+ [
+ "wham",
+ "whamg",
+ "wham-align"
+ ],
+ [
+ "mergeprofiledirectory.py",
+ "wiggletoolsindex.py",
+ "wiggletools",
+ "mergeprofilesdirectory.py",
+ "mergebedlikedirectory.sh",
+ "multijob.py",
+ "wiggleplots.py",
+ "mergebigwigdirectory.py"
+ ],
+ [
+ "estwise",
+ "promoterwise",
+ "wise",
+ "genomewise",
+ "scanwise_server",
+ "wise-doc",
+ "scanwise",
+ "estwisedb",
+ "dnal",
+ "genewisedb",
+ "wise24",
+ "wise-data",
+ "psw",
+ "dba",
+ "genewise",
+ "wise2",
+ "wisedata",
+ "pswdb"
+ ],
+ [
+ "wisecondorxgender",
+ "wisecondorx",
+ "wisecondorxpredict",
+ "wisecondorxconvert",
+ "wisecondorxnewref"
+ ],
+ [
+ "wtdbg",
+ "wtdbg2",
+ "wtdbg2-examples"
+ ],
+ [
+ "libmdc3t64",
+ "libmdc2",
+ "libmdc3",
+ "xmedcon",
+ "medcon",
+ "libmdc2-dev",
+ "libmdc-dev"
+ ],
+ [
+ "xpore",
+ "xpore.utils",
+ "xpore-doc",
+ "xpore.diffmod",
+ "xpore.scripts"
+ ],
+ [
+ "r-bioc-xvector",
+ "xvector"
+ ],
+ [
+ "yara_mapper",
+ "yara",
+ "yara_indexer"
+ ],
+ [
+ "rzerone",
+ "zerone"
+ ],
+ [
+ "zstdcat",
+ "pzstd",
+ "zstdmt",
+ "zstdgrep",
+ "zstdless",
+ "zstd",
+ "unzstd"
+ ]
+]
\ No newline at end of file
diff --git a/run_nlstruct.py b/run_nlstruct.py
new file mode 100644
index 0000000000000000000000000000000000000000..495b6fb36c27223f43942caabfb82071d30f8f70
--- /dev/null
+++ b/run_nlstruct.py
@@ -0,0 +1,66 @@
+# Main file to run nlstruct
+# Written by Clémence Sebe
+# October 2024
+
+from nlstruct.recipes import train_qualified_ner
+import os
+
+# List of the models
+scibert_uncased = 'allenai/scibert_scivocab_cased'
+bert_uncased = 'bert-base-uncased'
+
+tab_models = [bert_uncased, scibert_uncased]
+
+os.makedirs("models_train", exist_ok=True)
+
+#Path of the data
+# -"Ner_workflows_data/SoftCite_split"
+# -"Ner_workflows_data/BioToFlow_split"
+# -"Ner_workflows_data/Fusion_BioToFlow_SoftCite/with_just_conversion"
+# -"Ner_workflows_data/Fusion_BioToFlow_SoftCite/with_silver"
+
+path_data = "Ner_workflows_data/Fusion_BioToFlow_SoftCite/with_silver"
+
+# -------------------------------------------------------------------------------------
+
+# Vary random seeds
+for graine in [1,8,22,42,100]:
+
+ # Vary train/dev splits
+ for i in range (1,6):
+
+ for m in tab_models:
+ print(f"---------------------- Iteration {i} - Model {m}")
+
+ train = True
+ if 'scibert_scivocab_uncased' in m:
+ if f'scibert_iteration{i}_seed{graine}.pt' in os.listdir("models_train"):
+ train = False
+ else:
+ if f'bert_iteration{i}_seed{graine}.pt' in os.listdir("models_train"):
+ train = False
+
+ if train:
+ model = train_qualified_ner(
+ dataset={
+ "train": f"{path_data}/iteration_{i}/TRAIN",
+ "val" : f"{path_data}/iteration_{i}/VAL",
+ "test" : f"Ner_workflows_data/TEST_BioToFlow"
+ },
+ finetune_bert=False,
+ seed=graine,
+ bert_name=m,
+ fasttext_file="",
+ gpus=1, #0:cpu - 1:gpu
+ xp_name="my-xp",
+ return_model=True,
+ max_steps = 4000,
+ model_to_take_encoder="None",
+ )
+
+ if 'scibert_scivocab_uncased' in m:
+ model.save_pretrained(f'models_train/scibert_iteration{i}_seed{graine}.pt')
+ else:
+ model.save_pretrained(f'models_train/bert_iteration{i}_seed{graine}.pt')
+
+ os.system('rm checkpoints/*')