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"markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##**Final Project**\n", + "This is my Final Project of Learner's Space on Natural Language Processing, where I will be using some datasets and model for machine translation and then fine-tune the same model. I engaged in discussion with some of my friends who had taken this Learner's Space project as well, so that I can help them whenever they have a doubt and they help me when I have a doubt or run into an error." + ], + "metadata": { + "id": "nxLRozg71Twh" + } + }, + { + "cell_type": "markdown", + "source": [ + "First we give T4 GPU access and check whether the GPU is working or not using the code below" + ], + "metadata": { + "id": "engemglKJlAc" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "99bjwu4z1I8E", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "223a5c2f-1047-4b23-c6d7-84321751e1aa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mon Aug 14 15:53:22 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 60C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "source": [ + "After giving GPU access, we download necessary libraries like :\n", + "\n", + "\n", + "1. `transformers` for importing the pipelines and for tokenizer\n", + "2. `accelerate` for faster loading.\n", + "3. `sentencepiece` while applying the pipeline.\n", + "4. `gradio` for interactive app experience\n", + "5. `datasets` to import datasets\n", + "6. `evaluate` for loading the metric and evaluating\n", + "7. `sacrebleu` for the metric\n" + ], + "metadata": { + "id": "BAEBawzyJw2S" + } + }, + { + "cell_type": "code", + "source": [ + "! pip install -q transformers accelerate sentencepiece gradio datasets evaluate sacrebleu" + ], + "metadata": { + "id": "10En4sOM-7Zh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6f0312ff-9982-44a8-904c-fb46be8e788c" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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StepTraining Loss
5001.294000
10001.267000
15001.241400
20001.260000
25001.252200
30001.250200
35001.241500
40001.249200
45001.236400
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70001.120300
75001.118800
80001.119700
85001.125000
90001.122100
95001.127500
100001.115300
105001.130200
110001.131400
115001.127600
120001.123700
125001.122900
130001.085900
135001.049900
140001.060700
145001.052200
150001.060300
155001.054100
160001.051900
165001.055800
170001.057300
175001.066100
180001.058500
185001.060300
190001.048900

" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n", + "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n", + "Several commits (2) will be pushed upstream.\n", + "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n", + "Several commits (3) will be pushed upstream.\n", + "WARNING:huggingface_hub.repository:Several commits (3) will be pushed upstream.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=19020, training_loss=1.1422517776489258, metrics={'train_runtime': 4588.4073, 'train_samples_per_second': 132.641, 'train_steps_per_second': 4.145, 'total_flos': 1.3068977520771072e+16, 'train_loss': 1.1422517776489258, 'epoch': 3.0})" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "##**Saving in Drive**\n", + "Here after training the model, we save it into google drive which can be accessed later if we need to build gradio interface" + ], + "metadata": { + "id": "UhZ0aT0OuFQN" + } + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MVIbmwFGkEWC", + "outputId": "4b12d218-7633-4848-9603-f1c93acdc9e7" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save_pretrained('/content/drive/MyDrive/LS_NLP_Parth')" + ], + "metadata": { + "id": "R0a9GwdPkRTa" + }, + "execution_count": 23, + "outputs": [] + } + ] +} \ No newline at end of file