{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "rilo8nzE4L2F"
},
"source": [
"If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers as well as some other libraries. Uncomment the following cell and run it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "NWl3z9tR4Un1"
},
"outputs": [],
"source": [
"# Install\n",
"!pip install -q biopython transformers datasets huggingface_hub accelerate"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "8XJthJun4mVw"
},
"source": [
"If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n",
"\n",
"To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n",
"\n",
"First you have to login to the huggingface hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "AUY6jNR85SbG"
},
"source": [
"Then you need to install Git-LFS. Uncomment the following instructions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "439dGQnD5Uul",
"outputId": "18c8291c-0af0-4f96-f360-7af4e49f21e2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"git-lfs is already the newest version (2.9.2-1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n"
]
}
],
"source": [
"!apt install git-lfs"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "vYX1k_HIHqbE"
},
"source": [
"# **Fine-Tuning the Nucleotide-transformer**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "16RWKoAcPdLx"
},
"source": [
"The **Nucleotide Transformer** paper [Dalla-torre et al, 2023](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v2) introduces 4 genomics foundational models developed by **InstaDeep**. These transformers, of various sizes and trained on different datasets, allow powerful representations of DNA sequences that allow to tackle a very diverse set of problems such as chromatin accessibility, deleteriousness prediction, promoter and enhancer prediction etc... These representations can be extracted from the transformer and used as proxies of the DNA sequences (this is called probing) or the transformer can be trained further on a specific task (this is called finetuning)."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "_QJ13X_uPfSb"
},
"source": [
""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "AqGW_WWzn8TZ"
},
"source": [
"This notebook allows you to fine-tune these models.\n",
"\n",
"The model we are going to use is the [500M Human Ref model](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g), which is a 500M parameters transformer pre-trained on the human reference genome, per the training methodology presented in the Nucleotide Transformer Paper. It is one of the 4 models introduced, all available on the [Instadeep HuggingFace page](https://huggingface.co/InstaDeepAI):\n",
"\n",
"```\n",
"| Model name | Num layers | Num parameters | Training dataset |\n",
"|---------------------|------------|----------------|------------------------|\n",
"| `500M Human Ref` | 24 | 500M | Human reference genome |\n",
"| `500M 1000G` | 24 | 500M | 1000G genomes |\n",
"| `2.5B 1000G` | 32 | 2.5B | 1000G genomes |\n",
"| `2.5B Multispecies` | 32 | 2.5B | Multi-species dataset |\n",
"\n",
"```\n",
"\n",
"Note that using the larger models will require more GPU memory and produce longer finetuning times\n",
"\n",
"In the following, we showcase the nucleotide transformer ability to classify genomic sequences as two of the most basic genomic motifs: **promoters** and **enhancers types**. Both of them are classification task, but the enhancers types task is much more challenging with its 3 classes.\n",
"\n",
"These two tasks are still very basic, but the nucleotide transformers have been shown to beat/match state of the art models on much more complex tasks such as [DeepSEA](https://www.nature.com/articles/nmeth.3547), which, given a DNA sequence, predicts 919 chromatin profiles from a diverse set of human cells and tissues from a single sequence or [DeepSTARR](https://www.nature.com/articles/s41588-022-01048-5), which predicts an enhancer's activity.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "i2rtkspVfXFw"
},
"source": [
"## **Importing required packages**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "BKb8ZufeHccM"
},
"source": [
"### **Import and install**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "S_dMf_n-Epvp"
},
"outputs": [],
"source": [
"# Imports\n",
"from transformers import AutoTokenizer, TrainingArguments, Trainer, AutoModelForSequenceClassification\n",
"import torch\n",
"from sklearn.metrics import matthews_corrcoef, f1_score\n",
"from sklearn.model_selection import train_test_split\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "I7NSdbS-O6DI"
},
"outputs": [],
"source": [
"from accelerate.test_utils.testing import get_backend\n",
"\n",
"device, _, _ = get_backend()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "uuM3JB7SH45T"
},
"source": [
"### **Prepare and create the model for fine-tuning**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "ch7deTIvWrjF"
},
"source": [
"The nucleotide transformer will be fine-tuned on two **classification tasks**: **promoter** and **enhancer types** classification.\n",
"The `AutoModelForSequenceClassification` module automatically loads the model and adds a simple classification head on top of the final embeddings."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "k0shQr8_pu2s"
},
"source": [
"## **First task : Promoter prediction**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "ykhgl7B7-3EC"
},
"source": [
"Promoter prediction is a **sequence classification** problem, in which the DNA sequence is predicted to be either a promoter or not.\n",
"\n",
"A promoter is a region of DNA where transcription of a gene is initiated. Promoters are a vital component of expression vectors because they control the binding of RNA polymerase to DNA. RNA polymerase transcribes DNA to mRNA which is ultimately translated into a functional protein\n",
"\n",
" This task was introduced in [DeePromoter](https://www.frontiersin.org/articles/10.3389/fgene.2019.00286/full), where a set of TATA and non-TATA promoters was gathered. A negative sequence was generated from each promoter, by randomly sampling subsets of the sequence, to guarantee that some obvious motifs were present both in the positive and negative dataset.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QyaFCHqqQwpM"
},
"outputs": [],
"source": [
"num_labels_promoter = 2\n",
"# Load the model\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"InstaDeepAI/nucleotide-transformer-500m-human-ref\", num_labels=num_labels_promoter)\n",
"model = model.to(device)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "HmIVmKByPk_T"
},
"source": [
"### **Dataset loading and preparation**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jGmNDcaKnoW6",
"outputId": "cd2081f5-b51c-4694-a42d-7d96060bbe37"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:datasets.builder:Found cached dataset nucleotide_transformer_downstream_tasks_public (/root/.cache/huggingface/datasets/InstaDeepAI___nucleotide_transformer_downstream_tasks_public/promoter_all/0.0.0/d649d80b49e7b062da8a12a4d80a5d636571467e76a0a036d89078ffded1e5c9)\n",
"WARNING:datasets.builder:Found cached dataset nucleotide_transformer_downstream_tasks_public (/root/.cache/huggingface/datasets/InstaDeepAI___nucleotide_transformer_downstream_tasks_public/promoter_all/0.0.0/d649d80b49e7b062da8a12a4d80a5d636571467e76a0a036d89078ffded1e5c9)\n"
]
}
],
"source": [
"from datasets import load_dataset, Dataset\n",
"\n",
"# Load the promoter dataset from the InstaDeep Hugging Face ressources\n",
"dataset_name = \"promoter_all\"\n",
"train_dataset_promoter = load_dataset(\n",
" \"InstaDeepAI/nucleotide_transformer_downstream_tasks\",\n",
" dataset_name,\n",
" split=\"train\",\n",
" streaming= False,\n",
" )\n",
"test_dataset_promoter = load_dataset(\n",
" \"InstaDeepAI/nucleotide_transformer_downstream_tasks\",\n",
" dataset_name,\n",
" split=\"test\",\n",
" streaming= False,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "G0N81WU4_r_l"
},
"outputs": [],
"source": [
"# Get training data\n",
"train_sequences_promoter = train_dataset_promoter['sequence']\n",
"train_labels_promoter = train_dataset_promoter['label']\n",
"\n",
"# Split the dataset into a training and a validation dataset\n",
"train_sequences_promoter, validation_sequences_promoter, train_labels_promoter, validation_labels_promoter = train_test_split(train_sequences_promoter,\n",
" train_labels_promoter, test_size=0.05, random_state=42)\n",
"\n",
"# Get test data\n",
"test_sequences_promoter = test_dataset_promoter['sequence']\n",
"test_labels_promoter = test_dataset_promoter['label']"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "bJYXsXPamVmq"
},
"source": [
"Let us have a look at the data. If we extract the last sequence of the dataset, we see that it is indeed a promoter, as its label is 1. Furthermore, we can also see that it is a TATA promoter, as the TATA motif is present at the 221th nucleotide of the sequence!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BRyL7nw6mDBF",
"outputId": "9fc7c6f5-bc84-4fb0-cea0-2aec6f2dabf8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The DNA sequence is CACACCAGACAAAATTTGGTTAATTTGCGCCCAATATTCATTACTTTGACCTAACCTTTGTTCTGAAGGCCGTGTACAAGGACAAGGCCCTGAGATTATTGCAACAGTAACTTGAAAAACTTTCAGAAGTCTATTCTGTAGGATTAAAGGAATGCTGAGACTATTCAAGTTTGAAGTCCTGGGGGTGGGGAAAAATAAAAAACCTGTGCTAGAAAGCTTAGTATAGCATGTAACTTTAGAGTCCTGTGGAGTCCTGAGTCTCCCACAGACCAGAACAGTCATTTAAAAGTTTTCAGGAAA.\n",
"Its associated label is label 1.\n",
"This promoter is a TATA promoter, as the TATA motif is present at the 221th nucleotide.\n"
]
}
],
"source": [
"idx_sequence = -1\n",
"sequence, label = train_sequences_promoter[idx_sequence], train_labels_promoter[idx_sequence]\n",
"print(f\"The DNA sequence is {sequence}.\")\n",
"print(f\"Its associated label is label {label}.\")\n",
"\n",
"idx_TATA = sequence.find(\"TATA\")\n",
"\n",
"print(f\"This promoter is a TATA promoter, as the TATA motif is present at the {idx_TATA}th nucleotide.\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "LvWLmFNjb2e0"
},
"source": [
"### **Tokenizing the datasets**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "CZWBIP8WUqLc"
},
"source": [
"All inputs to neural nets must be numerical. The process of converting strings into numerical indices suitable for a neural net is called **tokenization**."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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"7fd6f4b9269b4472b1816c4421fcc6de",
"c1aada443f4c455796baaddf4a2601df",
"da8d9fe59e354fd6b5275a5b1db2b042",
"20a59fb6437d428ead7804d1d11b5a85",
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"a3de9e2b6ee34a6aba03e3c0b57d748a",
"663cc040b34743819d963e96516b4923",
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},
"id": "tLebn4WTOUvO",
"outputId": "011cc2bf-8f49-4e27-a07d-9c753b8f0179"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "41f0bd944b344306b2d733d7fd4f774b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/129 [00:00\n",
" \n",
" \n",
" [1000/1000 24:16, Epoch 0/1]\n",
" \n",
"
\n",
" \n",
"
\n",
"
Step
\n",
"
Training Loss
\n",
"
Validation Loss
\n",
"
F1 Score
\n",
"
\n",
" \n",
" \n",
"
\n",
"
50
\n",
"
0.537400
\n",
"
0.474810
\n",
"
0.716025
\n",
"
\n",
"
\n",
"
100
\n",
"
0.382600
\n",
"
0.541949
\n",
"
0.829284
\n",
"
\n",
"
\n",
"
150
\n",
"
0.306800
\n",
"
0.239439
\n",
"
0.917620
\n",
"
\n",
"
\n",
"
200
\n",
"
0.277000
\n",
"
0.258893
\n",
"
0.920110
\n",
"
\n",
"
\n",
"
250
\n",
"
0.382500
\n",
"
0.339054
\n",
"
0.900000
\n",
"
\n",
"
\n",
"
300
\n",
"
0.251800
\n",
"
0.297043
\n",
"
0.902489
\n",
"
\n",
"
\n",
"
350
\n",
"
0.271700
\n",
"
0.229136
\n",
"
0.913751
\n",
"
\n",
"
\n",
"
400
\n",
"
0.264300
\n",
"
0.252098
\n",
"
0.925468
\n",
"
\n",
"
\n",
"
450
\n",
"
0.375900
\n",
"
0.203039
\n",
"
0.924782
\n",
"
\n",
"
\n",
"
500
\n",
"
0.242500
\n",
"
0.185888
\n",
"
0.925326
\n",
"
\n",
"
\n",
"
550
\n",
"
0.293500
\n",
"
0.255502
\n",
"
0.928889
\n",
"
\n",
"
\n",
"
600
\n",
"
0.209800
\n",
"
0.287625
\n",
"
0.920796
\n",
"
\n",
"
\n",
"
650
\n",
"
0.205400
\n",
"
0.232000
\n",
"
0.933126
\n",
"
\n",
"
\n",
"
700
\n",
"
0.221300
\n",
"
0.245803
\n",
"
0.929134
\n",
"
\n",
"
\n",
"
750
\n",
"
0.270400
\n",
"
0.221323
\n",
"
0.936412
\n",
"
\n",
"
\n",
"
800
\n",
"
0.243600
\n",
"
0.229891
\n",
"
0.939006
\n",
"
\n",
"
\n",
"
850
\n",
"
0.281000
\n",
"
0.210025
\n",
"
0.933492
\n",
"
\n",
"
\n",
"
900
\n",
"
0.158600
\n",
"
0.202798
\n",
"
0.939675
\n",
"
\n",
"
\n",
"
950
\n",
"
0.205100
\n",
"
0.210799
\n",
"
0.937712
\n",
"
\n",
"
\n",
"
1000
\n",
"
0.159900
\n",
"
0.202635
\n",
"
0.940362
\n",
"
\n",
" \n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_results = trainer.train()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "Z3PpedkYwlrf"
},
"source": [
"Note that the finetuning is done with a small batch size (8). The training time can be reduced by increasing the batch size, as it leverages parallelism in the GPU."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "6EPTSl90w2Ii"
},
"source": [
"#### **Validation F1 score**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "r226L3tsxSLC"
},
"outputs": [],
"source": [
"curve_evaluation_f1_score =[[a['step'],a['eval_f1_score']] for a in trainer.state.log_history if 'eval_f1_score' in a.keys()]\n",
"eval_f1_score = [c[1] for c in curve_evaluation_f1_score]\n",
"steps = [c[0] for c in curve_evaluation_f1_score]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "qJ2vpg18xUx9",
"outputId": "9e647f1c-5e23-4553-9b36-8b36c5155032"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(steps, eval_f1_score, 'b', label='Validation F1 score')\n",
"plt.title('Validation F1 score for promoter prediction')\n",
"plt.xlabel('Number of training steps performed')\n",
"plt.ylabel('Validation F1 score')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "vP1kiYt2yryg"
},
"source": [
"#### **F1 score on the test dataset**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "snSdCSXDa-A-",
"outputId": "b386ab77-869c-4d35-971a-b5790ed65e4c"
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"F1 score on the test dataset: 0.9388458225667529\n"
]
}
],
"source": [
"# Compute the F1 score on the test dataset :\n",
"print(f\"F1 score on the test dataset: {trainer.predict(tokenized_datasets_test_promoter).metrics['test_f1_score']}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "Iq_8Eyj0EUCl"
},
"source": [
"For the promoter prediction task, we obtain a perforance that is already close to the one displayed in the [**article**](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1.full.pdf) by training on only 1000 steps. A F1 score of **0.938** is obtained after just **1000 training steps**. To get closer to the **0.954** score obtained in the nucleotide transformer paper after 10,000 training steps, we surely need to train for longer!"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "eIhNXdibvZ5y"
},
"source": [
"## **Second task : Enhancer type prediction**"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "cYDOcWC5BPDh"
},
"source": [
"In this section, we fine-tune the nucleotide transformer model on **enhancer type prediction**, which consists in classifying a DNA sequence as **strong**, **weak** or **non enhancer**.\n",
"\n",
"In genetics, an enhancer is a short (50–1500 bp) region of DNA that can be bound by proteins (activators) to increase the likelihood that transcription of a particular gene will occur.\n",
"\n",
"[A deep learning framework for enhancer prediction using word embedding and sequence generation](https://www.sciencedirect.com/science/article/abs/pii/S0301462222000643) introduced the dataset used here by augmenting an original set of enhancers with 6000 synthetic enhancers and 6000 synthetic\n",
"non-enhancers produced through a generative model."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "p3rUywfA06uN"
},
"source": [
"# Model"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IzThqr4Q051a",
"outputId": "cf346d32-ceb2-4442-c7e4-d08e4bddba0a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at InstaDeepAI/nucleotide-transformer-500m-human-ref were not used when initializing EsmForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.bias']\n",
"- This IS expected if you are initializing EsmForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing EsmForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at InstaDeepAI/nucleotide-transformer-500m-human-ref and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"num_labels_enhancers_types = 3\n",
"# Load the model\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"InstaDeepAI/nucleotide-transformer-500m-human-ref\", num_labels=num_labels_enhancers_types)\n",
"model = model.to(device)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "4RZ5xIwZ8ZEU"
},
"source": [
"### **Dataset loading and preparation**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "socJrDpZPf90",
"outputId": "8fb92597-bf24-478f-a7c6-0aa8050cc298"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:datasets.builder:Found cached dataset nucleotide_transformer_downstream_tasks (/root/.cache/huggingface/datasets/InstaDeepAI___nucleotide_transformer_downstream_tasks/enhancers_types/0.0.0/4a78b0644424e03fb4f26af3966a46e57ea50a1132ab8bb2f63b7808ce6a8772)\n",
"WARNING:datasets.builder:Found cached dataset nucleotide_transformer_downstream_tasks (/root/.cache/huggingface/datasets/InstaDeepAI___nucleotide_transformer_downstream_tasks/enhancers_types/0.0.0/4a78b0644424e03fb4f26af3966a46e57ea50a1132ab8bb2f63b7808ce6a8772)\n"
]
}
],
"source": [
"from datasets import load_dataset, Dataset\n",
"\n",
"# Load the enhancers dataset from the InstaDeep Hugging Face ressources\n",
"dataset_name = \"enhancers_types\"\n",
"train_dataset_enhancers = load_dataset(\n",
" \"InstaDeepAI/nucleotide_transformer_downstream_tasks\",\n",
" dataset_name,\n",
" split=\"train\",\n",
" streaming= False,\n",
" )\n",
"test_dataset_enhancers = load_dataset(\n",
" \"InstaDeepAI/nucleotide_transformer_downstream_tasks\",\n",
" dataset_name,\n",
" split=\"test\",\n",
" streaming= False,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "PJ0iVqLhPj4C"
},
"outputs": [],
"source": [
"# Get training data\n",
"train_sequences_enhancers = train_dataset_enhancers['sequence']\n",
"train_labels_enhancers = train_dataset_enhancers['label']\n",
"\n",
"# Split the dataset into a training and a validation dataset\n",
"train_sequences_enhancers, validation_sequences_enhancers, train_labels_enhancers, validation_labels_enhancers = train_test_split(train_sequences_enhancers,\n",
" train_labels_enhancers, test_size=0.10, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "Z9JR5eVXP5DX"
},
"outputs": [],
"source": [
"# Get test data\n",
"test_sequences_enhancers = test_dataset_enhancers['sequence']\n",
"test_labels_enhancers = test_dataset_enhancers['label']"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "OJORQ7k57aJE"
},
"source": [
"### **Tokenizing the datasets**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "sorZmGLY7lpq"
},
"outputs": [],
"source": [
"# Enhancer dataset\n",
"ds_train_enhancers = Dataset.from_dict({\"data\": train_sequences_enhancers,'labels':train_labels_enhancers})\n",
"ds_validation_enhancers = Dataset.from_dict({\"data\": validation_sequences_enhancers,'labels':validation_labels_enhancers})\n",
"ds_test_enhancers = Dataset.from_dict({\"data\": test_sequences_enhancers,'labels':test_labels_enhancers})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
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]
},
"id": "97i-0HvQ7uEa",
"outputId": "6487b23e-8fa9-4919-a7d6-48d2ee6d68c8"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "105d79d93d564f5a97966aea6eda96e9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/13471 [00:00\n",
" \n",
" \n",
" [1000/1000 11:50, Epoch 0/1]\n",
" \n",
"