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(73/73), done.\u001b[K\n", "remote: Compressing objects: 100% (56/56), done.\u001b[K\n", "remote: Total 73 (delta 32), reused 41 (delta 11), pack-reused 0\u001b[K\n", "Unpacking objects: 100% (73/73), 1.89 MiB | 4.48 MiB/s, done.\n" ] } ], "source": [ "!git clone https://github.com/kubapok/cnlps-caiccaic.git" ] }, { "cell_type": "code", "source": [ "!pip install -Uq datasets transformers peft bitsandbytes loralib accelerate" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1qS0p25llt37", "outputId": "e77b4672-c85f-4a5c-9e98-7de4070267a0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 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"\u001b[?25h" ] } ] }, { "cell_type": "markdown", "source": [ "# Create dataset" ], "metadata": { "id": "rDDe5DJCy2r_" } }, { "cell_type": "code", "source": [ "!cat cnlps-caiccaic/train/in.tsv cnlps-caiccaic/dev-A/in.tsv > in.tsv\n", "!cat cnlps-caiccaic/train/expected.tsv cnlps-caiccaic/dev-A/expected.tsv > expected.tsv" ], "metadata": { "id": "OxO5rSflnWkT" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import csv\n", "from datasets import Dataset, DatasetDict\n", "\n", "with open('in.tsv', encoding='utf-8') as train_f_in, open('expected.tsv', encoding='utf-8') as train_f_exp:\n", " train_list_in = list(csv.reader(train_f_in, delimiter='\\t'))\n", " train_list_exp = train_f_exp.readlines()\n", "\n", "with open('cnlps-caiccaic/test-A/in.tsv', encoding='utf-8') as test_f_in:\n", " test_list_in = list(csv.reader(test_f_in, delimiter='\\t'))\n", "\n", "train_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': exp.strip().replace('}', ']').replace('{', '[').replace('\\t', '|')} for in_, exp in zip(train_list_in, train_list_exp)])\n", "test_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': ''} for in_ in test_list_in])\n", "dataset = DatasetDict({'train': train_data, 'test': test_data})" ], "metadata": { "id": "mOM2JAw4npqM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "dataset['train'][600]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bYmEQ35Rvjr-", "outputId": "23d6c786-d03a-4301-da8d-d50480af3888" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'text': \"don't alert me when an event in my calendar in location kenner begins language: en-US\",\n", " 'intent': \"Calendar|NotNotifyOnEventInLocation|['location': 'kenner']\"}" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "# Define training components" ], "metadata": { "id": "GeSaTKE0y4_d" } }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", "\n", "model_name = 'google/flan-t5-large'\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map='auto')" ], "metadata": { "id": "qt0MadQYlO_l", "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "36804dd34495452dad1dcdecee7c6006", "e790324c0d3f4720a8c600d7b3c0352d", "35e0331a5e664d00a7474a0c5633846d", "138844ef275b4a6b92d24990e47846e7", "55d88d908e664b8fb5d4aaa3df9a45df", "6c564d089e3c471897fd4aca12b8783e", "7797ad41dbdb4b10990b89c3adae141b", "5b4f8e68336b4c72bca898b980a00de4", "32adbd01b207482f98b2a7c42d66ae48", "ae3bdaed05bf48b1844765c0eadac847", "b4e15f63840a45f4be5bd63388bf6fe4", "46855a1011a44ffe98a4af6731c1c0ea", "0ef322e1b9674a308231f8359b6e2a37", "29812c1e0b2b4ec5bae4fc5b7ec5906e", "2a66bc36a8c948a5a6dd79661ffb8ea7", "434feed6330743feb918d2b4f20a852b", "5d544a1cf5b140249260fb6698bcac95", "b17fe70b4e1846d3bc598f8669b8bdb1", "a14aa0a6b7a441399877ad7e8a38137b", "f0514c2683bb42018e38a18e609c86cd", "89709f42e5224fb3bb2a88c34e58bca1", "50e074b7b73a4b5d822d798e21947762", "ff8dd4f2dc3f4e8d80cafe94bb14ea49", "83772061c4a44aa6a5b663108d08070e", "0acad7b2dbc34a35b4ac62c5cf8c57f9", "1cc936f1edeb410ebeaa16828afe4da5", "e0b415a7fc544ca4a7544a59f712b966", "590df66d2c7d40a59ff362f3e8c1a57c", "1548b3eabb4c47f4811b6af771b5b4f7", "f970c07e0912454ebd8b30a80681fce7", "48198c6bca7a475c8e62533e80b70ba5", "7fb0bb6d913e419989bc41d9064397ed", "b88175a9d9c648b5b5b35092d68f8346", "152a2400561040518048805ff3f37fc5", "268b349317c948a28c993968376dd331", "416f8540e05e492fb6badc55fb7b5ff4", "a98654cc1844488fa4348661f9086a0b", "1eabc4111eae481480f362dba641d322", "69061df3063f4de4a75fcb3a52c56285", "63420fd47ea24d3b8bd8532856551b24", "cf82fa8a8c09472a918b86700ae97bea", "c0426247be61440199fbe3bf2de11e24", "cd9fba630a914907a9fdc9adc04d9b2d", "df7dca53ab3340a3a0599440947fcac1", "d30362d5e51c41348cf7998c6716f53f", "5c9dc7a349d8498096bcebdd20338936", "49549c623ea245c19af928b8509b1a14", "6798d8bd2608481d9e72dda1a1c47e44", "d1a625388dc246018af0e5a17d4aa819", "bc8a5577c5904c3898099dc0afa8cbe1", "9fd49ca71655406d92ffc0449f9c3c51", "f978169bf5c34f3a9341212e26532c0e", "ea4da54ba189460c8fc95865647b00e4", "8d5ab085fdeb40a58174a93fc25b7eb8", "d9b4eac446274cdfa6915cbd7d2a7b3a", "3d93b25960d2482b8a5ae31f772ff3b3", "5d992a07a8ce43cfa5dd843728f37075", "a61a6d0c3a9b43159468e3ebf2f44ee0", "f395c83bd55443449af23ccc29fa5e18", "2ea35434cbf4451db22faf63900e255d", "fc721e11600248f692501fec9cd1c7c4", "c2b55cb44a064726823537251c298111", "2f5e8d2a03d84b619ab913a370cf0beb", "137ca2d2ace545a08206f0422b45d44f", "8ab44c4b2d29449bbbbf57d403e5f62d", "6e980c52e8c64103abb8805cb41b4389", "098530349bd643acaf1b3ce4d8378d38", "38f1803418004bd69036f94371bdb414", "13ac4f0841b14b1b87b2bf2daf3443bf", "8e19215e241c482f9fea7a36dc979531", "ef64e74b85d44f579dce6132c35452f4", "504c68ff3b8a4f00b0fa24fd563f39e7", "23e2628bd5514aa18b2b6777f191e24b", "7ee465d1d3c848e59389ad0a4da23a8a", "fca9eb4a0e2a4d248e3018006436da0f", "40c8ef6eb5f5420da2921f3805f973ca", "7aac275079ef43599fcc888b2123828c" ] }, "outputId": "3f2e2a79-3a8b-4bc9-eb03-835b637738c3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)okenizer_config.json: 0%| | 0.00/2.54k [00:00" ], "text/html": [ "\n", "
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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=12874, training_loss=0.06059208474804456, metrics={'train_runtime': 12136.5406, 'train_samples_per_second': 4.243, 'train_steps_per_second': 1.061, 'total_flos': 2.330193322605773e+16, 'train_loss': 0.06059208474804456, 'epoch': 1.0})" ] }, "metadata": {}, "execution_count": 23 } ] }, { "cell_type": "markdown", "source": [ "# Save the model" ], "metadata": { "id": "mDOXSmts1J-G" } }, { "cell_type": "code", "source": [ "trainer.model.save_pretrained('results_v2')\n", "tokenizer.save_pretrained('results_v2')" ], "metadata": { "id": "RN9rhtHw1KC9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!rm -r /content/drive/MyDrive/caiccaic; mkdir /content/drive/MyDrive/caiccaic\n", "!cp -r results_v2 /content/drive/MyDrive/caiccaic" ], "metadata": { "id": "FPUZs3SZPQ4z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# from google.colab import runtime\n", "# runtime.unassign()" ], "metadata": { "id": "em0VFt0rz3uJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Inference" ], "metadata": { "id": "ntt7iUnd8Wyc" } }, { "cell_type": "code", "source": [ "!cp -r /content/drive/MyDrive/results_v2 ." ], "metadata": { "id": "muGPGGT5m4Wx" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n", "import torch\n", "\n", "model = AutoModelForSeq2SeqLM.from_pretrained('results_v2', device_map={'':0})\n", "tokenizer = AutoTokenizer.from_pretrained('results_v2')\n", "model.eval()" ], "metadata": { "id": "hTshD-E_mv4p", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "30da8e10-c8fb-4d01-d558-07e8cb02d693" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "T5ForConditionalGeneration(\n", " (shared): Embedding(32128, 1024)\n", " (encoder): T5Stack(\n", " (embed_tokens): Embedding(32128, 1024)\n", " (block): ModuleList(\n", " (0): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " (relative_attention_bias): Embedding(32, 16)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseGatedActDense(\n", " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): NewGELUActivation()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (1-23): 23 x T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseGatedActDense(\n", " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): NewGELUActivation()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (final_layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (decoder): T5Stack(\n", " (embed_tokens): Embedding(32128, 1024)\n", " (block): ModuleList(\n", " (0): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " (relative_attention_bias): Embedding(32, 16)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseGatedActDense(\n", " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): NewGELUActivation()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (1-23): 23 x T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseGatedActDense(\n", " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): NewGELUActivation()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (final_layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n", ")" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "from datasets import load_from_disk\n", "from tqdm import tqdm\n", "\n", "def evaluate_peft_model(sample):\n", " outputs = model.generate(input_ids=torch.tensor(sample['input_ids']).unsqueeze(0).cuda(), max_new_tokens=512) \n", " prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n", " return prediction\n", "\n", "test_dataset = tokenized_dataset['test']\n", "\n", "predictions, references = [], []\n", "i = 0\n", "for sample in tqdm(test_dataset):\n", " p = evaluate_peft_model(sample)\n", " if i % 100 == 0:\n", " print('\\n', p)\n", " predictions.append(p)\n", " i += 1" ], "metadata": { "id": "4t0Epw1x8YOG", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a93e8654-603c-4353-bef6-04118302cf36" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\r 0%| | 0/10358 [00:00