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ritog/copy-of-robi-poems.ipynb

Created July 17, 2021 10:42
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"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
}
}
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ghosh-r/757c32d5b5065a91e63beb017e38dba9/copy-of-robi-poems.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dV9hhxEKF48s"
},
"source": [
"%%capture\n",
"! pip install datasets transformers"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MZ13avB4Ebnw"
},
"source": [
"# import re\n",
"import json\n",
"from sklearn.model_selection import train_test_split\n",
"import pandas as pd"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 132,
"referenced_widgets": [
"15cfa01ee073450886a880886604c562",
"9caabaee8a0a499f9db335174e2295d5",
"62462f8646464a38aa5fceb53af0c72b",
"724b8ea1b43647ba860325090ed99250",
"608a96da3b834b2dbee688614c041fbd",
"15031a8ce2c14dd8a1af5112abc4bc99",
"4557a319d0024a8f8d3d5e10137e8120",
"25fe9839fa41409fa1b8be4f9caa1dc4",
"2c2664b316e843d98e97ed6ddd78ad1d",
"87b54d2be7e44a14b71880aa7f0977fa",
"0e26bf1b7ff64064858b55f64c13e5c9",
"10067a00aa7f46e895a11ea42c1a0c83",
"5e51cc0230f9477ea229a9c8cf0a72c5",
"b7b5ac4bfa484e05bfb65ecd0fdcabec",
"90fe734421514632b7455707fdb6647a",
"fef095a5d6e047dcb79bff65a0b80124"
]
},
"id": "yGHBLbrGIWyV",
"outputId": "eb5296d4-3223-4dbc-de2c-be5bb8c70128"
},
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"ghosh-r/bangla-gpt2\")\n",
"\n",
"train_path = 'train.txt'\n",
"test_path = 'test.txt'"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "15cfa01ee073450886a880886604c562",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=881.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2c2664b316e843d98e97ed6ddd78ad1d",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1708956.0, style=ProgressStyle(descript…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1b4f-k8EIdXk",
"outputId": "f18dc59f-21cf-4268-8622-65f56430b036"
},
"source": [
"from transformers import TextDataset,DataCollatorForLanguageModeling\n",
"\n",
"def load_dataset(train_path,test_path,tokenizer):\n",
" train_dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=train_path,\n",
" block_size=128)\n",
" \n",
" test_dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=test_path,\n",
" block_size=128) \n",
" \n",
" data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer, mlm=False,\n",
" )\n",
" return train_dataset,test_dataset,data_collator\n",
"\n",
"train_dataset,test_dataset,data_collator = load_dataset(train_path,test_path,tokenizer)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/data/datasets/language_modeling.py:58: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py\n",
" FutureWarning,\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 120,
"referenced_widgets": [
"580bb65ee3824889b5631568b99a58c8",
"aac5891278e84c5291e5b0164044116e",
"66d20b10edfe4958a39368199fcaad3b",
"306b0cfe44594bca8befbf05d6deff51",
"072fc33d415b4421a069677ad61ff011",
"9e3c7fe1fff44b41af0798b7fe192f0f",
"94f04430f25f49beb366ab3ca728ec3a",
"6f4cca4a80274eadaf20d28acb074d6a"
]
},
"id": "FuETPslLIo4U",
"outputId": "51042dd7-674a-4cbc-868c-e33dda336ea7"
},
"source": [
"from transformers import Trainer, TrainingArguments,AutoModelWithLMHead\n",
"\n",
"model = AutoModelWithLMHead.from_pretrained(\"ghosh-r/bangla-gpt2\")\n",
"\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./gpt2-robi_kobita\", #The output directory\n",
" overwrite_output_dir=True, #overwrite the content of the output directory\n",
" num_train_epochs=3, # number of training epochs\n",
" per_device_train_batch_size=32, # batch size for training\n",
" per_device_eval_batch_size=64, # batch size for evaluation\n",
" eval_steps = 400, # Number of update steps between two evaluations.\n",
" save_steps=800, # after # steps model is saved \n",
" warmup_steps=500,# number of warmup steps for learning rate scheduler\n",
" prediction_loss_only=True,\n",
" )\n",
"\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=test_dataset,\n",
")"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:847: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "580bb65ee3824889b5631568b99a58c8",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=510401385.0, style=ProgressStyle(descri…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 347
},
"id": "j-t8_0pjZQiu",
"outputId": "9283797a-2d02-4b69-bc37-2e665deeaf12"
},
"source": [
"trainer.train()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"***** Running training *****\n",
" Num examples = 6888\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 648\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='648' max='648' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [648/648 06:08, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>2.350000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=648, training_loss=2.242282631956501, metrics={'train_runtime': 369.4134, 'train_samples_per_second': 55.937, 'train_steps_per_second': 1.754, 'total_flos': 1974853779849216.0, 'train_loss': 2.242282631956501, 'epoch': 3.0})"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jBRFaebLd9NA",
"outputId": "adf8f2f6-1fb5-4966-cb5d-413f363d1884"
},
"source": [
"trainer.save_model()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"Saving model checkpoint to ./gpt2-robi_kobita\n",
"Configuration saved in ./gpt2-robi_kobita/config.json\n",
"Model weights saved in ./gpt2-robi_kobita/pytorch_model.bin\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PBW6W2D-d0HJ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a817202f-34e6-4e4d-ec17-bde31dedae4d"
},
"source": [
"from transformers import pipeline\n",
"\n",
"poet = pipeline('text-generation',model='./gpt2-robi_kobita', tokenizer='ghosh-r/bangla-gpt2')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"loading configuration file ./gpt2-robi_kobita/config.json\n",
"Model config GPT2Config {\n",
" \"_name_or_path\": \"ghosh-r/bangla-gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.0,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.0,\n",
" \"eos_token_id\": 50256,\n",
" \"gradient_checkpointing\": false,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"resid_pdrop\": 0.0,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.8.2\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
"loading configuration file ./gpt2-robi_kobita/config.json\n",
"Model config GPT2Config {\n",
" \"_name_or_path\": \"ghosh-r/bangla-gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.0,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.0,\n",
" \"eos_token_id\": 50256,\n",
" \"gradient_checkpointing\": false,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"resid_pdrop\": 0.0,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.8.2\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
"loading weights file ./gpt2-robi_kobita/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
"\n",
"All the weights of GPT2LMHeadModel were initialized from the model checkpoint at ./gpt2-robi_kobita.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
"Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
"loading configuration file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/d916cda1fc7c33b497e404011afd7185469a7d6daf8d674e4bcbdb951e059768.0d65f2cde7eca11af939c04f8bd685073e094591ded3eb632f51dc0e29899bde\n",
"Model config GPT2Config {\n",
" \"_name_or_path\": \"ghosh-r/bangla-gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.0,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.0,\n",
" \"eos_token_id\": 50256,\n",
" \"gradient_checkpointing\": false,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"resid_pdrop\": 0.0,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.8.2\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/vocab.json from cache at None\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/merges.txt from cache at None\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/8b3d4a6f9e80e0e5494bf8f1754de822dcdc06314d0994c9b68f7dbc1735cffa.9dfff3c35c0a768eb26244f921a71293e1adaa890f1468c982bad5568d6cd623\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/added_tokens.json from cache at None\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/special_tokens_map.json from cache at None\n",
"loading file https://huggingface.co/ghosh-r/bangla-gpt2/resolve/main/tokenizer_config.json from cache at None\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SPzHEeeUeHJZ",
"outputId": "aba9c81d-3f0e-4882-8ec8-ae95a2ce8c99"
},
"source": [
"poem = poet('আমি তোমাকে দেখেছি মোর হৃদয়ে', max_length=200)"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"id": "oIOlTCTdxyKK",
"outputId": "c9742aca-b5d4-4b6f-bac1-869183ae4e5a"
},
"source": [
"poem[0]['generated_text']"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'আমি তোমাকে দেখেছি মোর হৃদয়ের কুকুরের মতো।\\n\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0 মুহূর্তে সব নেভায়ে,\\n\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0 চিরদিন থাকে কেন আমার ঘরে?\\n\\xa0\\xa0\\xa0জীবন আমার হৃদয়ের পালঙ্কের একেবারে কাছে,\\n\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0আমার হৃদয়-সমুদ্র পারে।\\n\\xa0\\xa0\\xa0মনে হত আমার কত কত সংসারে\\n\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0\\xa0কত রাত্\\u200cরির কত বিহঙ্গ যেন হয়।\\n\\xa0\\xa0মনে হত পৃথিবীর কত মা যেন'"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GMvtF1Vg2oBk"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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