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nltk\n", "from nltk.corpus import stopwords\n", "\n", "nltk.download('stopwords')\n", "nltk.download('punkt')\n", "\n", "def remove_punctuation_and_stopwords(sent):\n", " # Remove punctuation\n", " sent = sent.translate(str.maketrans('', '', string.punctuation))\n", "\n", " # Tokenize the sentence\n", " tokens = nltk.word_tokenize(sent)\n", "\n", " # Remove stop words\n", " stop_words = set(stopwords.words('english'))\n", " tokens = [token for token in tokens if token.lower() not in stop_words]\n", "\n", " # Join the tokens back into a sentence\n", " sent = ' '.join(tokens)\n", "\n", " return sent\n", "\n", "# Example usage\n", "sent = \"This is a sentence. It has punctuation and stop words.\"\n", "sent = remove_punctuation_and_stopwords(sent)\n", "print(sent) # Output: \"sentence punctuation stop words\"\n" ], "metadata": { "id": "mW2iNa0aMMJ6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install datasets evaluate seqeval" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Vc1laayhjVbR", "outputId": "3091efd6-7c4d-4578-9a13-81094aa4d8ff" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.18.0)\n", "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.1)\n", "Requirement already satisfied: seqeval in /usr/local/lib/python3.10/dist-packages (1.2.2)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.3)\n", "Requirement already satisfied: numpy>=1.17 in 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satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n", "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.2)\n", "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.2)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n", 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"https://localhost:8080/", "height": 145, "referenced_widgets": [ "014c9bc0a96c4d84b591680bc0ea9cd1", "e01913638285433a8a88f0897c5cdfb7", "00e7c403fbd648c685a52fabf0fefd4d", "7327ec8d378a4701887c264ed47c273e", "3a123190eee84f24971fe70989a2cc30", "e8052b48b5d64f17825e561a1cb07808", "0280f5e1b3f54bf08ab3fe4f589d6cf0", "120995429b8f4bb8a5d6d4c8eaab72e9", "d486b8c62a354b17a757080a11785083", "168b61613e974caa997569ade26b92ba", "05bcb25b4ef54690ba04ad44007c8512", "66d68f31077946feaa68c8dad57520d2", "8226dbc61bb34fc4986d2e911334e901", "c461783efe6344dca6b6424ddd7eb764", "da250f704a484cda835aba323145a672", "14f09b9a26a6417a9b8afd6250e98390", "b3bf77c2b62342838a90e7f6219a9fcb", "56bb71ac717346e19ba77745abc27d63", "9c329d920dc54c67ad1c4ddbf7a1f1c3", "0014505744504ada8af612225f0c6a1e", "c5e868801cee452996340508fbb75d7c", "1399178c2ff14e3a9ba8bf346edd6aff", "3a8d6eb73f4248e2a959bc55a2df77df", "daef153e621e40a2a87e03bf68fe68bc", "aa67c01a38ee4802a238ec310dc6b5ae", "a4f2365752e1445ba2970cfaaf617c8e", "9776fdf7be9c4297b156dfe7cf4616a1", "26307b61c47544319d0df63fcb84da7f", "27be0374cc9640a3b39ab23d82ac7654", "330cf889c0bf431f9363d97737bc81e2", "e32d681198ef44f6b0af0d8c64eaa678", "1fe85d9e150844749000b3a8822b5a23" ] }, "outputId": "558d62c3-1042-4a22-d007-9a942c67b188" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
1743\u001b[0;31m \u001b[0mexample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_example\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1744\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/features/features.py\u001b[0m in 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following exception:\n", "\u001b[0;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mshipping_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"harsh13333/shipping_label_ner\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrust_remote_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m 2580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2581\u001b[0m \u001b[0;31m# Download and prepare data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2582\u001b[0;31m builder_instance.download_and_prepare(\n\u001b[0m\u001b[1;32m 2583\u001b[0m \u001b[0mdownload_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2584\u001b[0m \u001b[0mdownload_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36mdownload_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnum_proc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_proc\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_proc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m self._download_and_prepare(\n\u001b[0m\u001b[1;32m 1006\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1007\u001b[0m \u001b[0mverification_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverification_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1766\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_download_and_prepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverification_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_splits_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1767\u001b[0;31m super()._download_and_prepare(\n\u001b[0m\u001b[1;32m 1768\u001b[0m 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\u001b[0;34m**\u001b[0m\u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1101\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1102\u001b[0m raise OSError(\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1603\u001b[0m \u001b[0mjob_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m for job_id, done, content in self._prepare_split_single(\n\u001b[0m\u001b[1;32m 1606\u001b[0m \u001b[0mgen_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgen_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjob_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_prepare_split_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1607\u001b[0m ):\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1760\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSchemaInferenceError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__context__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1761\u001b[0m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDatasetGenerationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"An error occurred while generating the dataset\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1764\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mjob_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtotal_num_examples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_num_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_shards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshard_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset" ] } ] }, { "cell_type": "markdown", "source": [ "### explore the data" ], "metadata": { "id": "7sKB7uoSmxgH" } }, { "cell_type": "code", "source": [ "shipping_labels" ], "metadata": { "id": "a_wjBRRblyiw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "shipping_labels['train']" ], "metadata": { "id": "thYawHNgm4yJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "shipping_labels['train'].to_pandas()" ], "metadata": { "id": "xP1acn9em6zO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### show the labels list\n", "label_list = shipping_labels[\"train\"].features[f\"ner_tags\"].feature.names\n", "label_list" ], "metadata": { "id": "9i-MD-yim_94" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Model Creation\n", "- used models\n", "1. xlm-roberta-large\n", "2. distilbert-base-cased\n", "3. bert-base-uncased\n", "4. bert-base-cased" ], "metadata": { "id": "mFFs1D8cnW8d" } }, { "cell_type": "code", "source": [ "\n", "from transformers import AutoTokenizer\n", "\n", "tokenizer_roberta = AutoTokenizer.from_pretrained(\"xlm-roberta-large\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "c18b4e07a5634929bb1cf1869b5bd3cd", "e16d47fc272444c5804ed52924092a54", "d95bf93709a54bd0a4003eb7d0c54063", "b90ba9ba331f4fea9915183527425152", "ce33d4e563e443b99ebb9b70f508b2f7", "7f0872cc54304ff7839fd52c390ab23a", "cd54d91069034b1ab8d54277242e1212", "65d197e11c77469d9d05a054e63fba77", "36c979ba9b374a64870941b6c269c70f", "ed4f92c51cbe4416b27d5b2e06a61ff8", "4498ec5e15814967be17503536acd8fa", "ba981d27be0a434fb215169036ca4d47", "f837a0b50aa84bc58d256ef0434c101d", "222adb2c788e4dc5964d005b6752dab8", "b4908d98da594c1e8932cdcc63d3fa53", "c9a7bba8cef94fc3ac38fededc4206b6", "52bcf8dfa4964e0aa3bc882c3b01babf", "52a28fd53a984c829f24883f612e9264", "1c4207338d5c41af91975f4eba907c06", "54d17297d0054f2f97c866896fc275f0", "9cba19df4b044452bf585d2ef1066a5f", "78b7a1ee87f340439a6c8f53c217ba17", "f19c8a98bc494fb294a27b5d180877f1", "dec6e5de29f744aeadf716d91af52bf0", "7e639de76dc94ac68b3f1ead715c0215", "482a10e359f848039a796e2ed4fad03b", "e851d46ff72d4843a974f6e3e4962542", "6d499c9ca96349f6a78a55069af586af", "2ed8f78c1a414b93b4293ef3a2e368f9", "f57eee3bcf924f77a553c792431608ad", "7e390da8f4134b58b3ac0351bdf0729c", "7f3d8132f798405c8359ddfb886e448b", "49fee89388804ee79d0574f5aa7ac928", "3dafad27f524422c848165dcd0baa9eb", "4aec17e7b4f34c77a016202dc32027a6", "d3b0c0b11c3845289e976d635352c712", "47b4097d57c8466193037605b7c9a8ab", "714eec46a29f4896aa83924f0c200fac", "a494c37e39df4f91b09507f79eea7a6a", "855be793ef9e41a99b99ade4b95c1fe3", "b51c0a2e93864935bdbd2308e478a47e", "2917b399c7bf4599a614a5314cb3f9b2", "fae1dc5f82044cc5bbe863285dd87da6", "a2e32d2b66604c95a5b4c219e892b675" ] }, "id": "c9_8Ti3NnQqW", "outputId": "7cd084ba-bc66-47b4-9a32-481fcb02151d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/25.0 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokenized_bert_uncased\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshipping_labels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_and_align_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'shipping_labels' is not defined" ] } ] }, { "cell_type": "code", "source": [ "from transformers import DataCollatorForTokenClassification\n", "#add tokenizer\n", "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer_bert_uncased,)" ], "metadata": { "id": "ytI_JcWEtE45" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Train the model" ], "metadata": { "id": "6wuqpPiQvNUz" } }, { "cell_type": "code", "source": [ "import evaluate\n", "\n", "seqeval = evaluate.load(\"seqeval\")" ], "metadata": { "id": "4PvDUBSxvHH_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "example = shipping_labels[\"train\"][0]['ner_tags']\n", "labels = [label_list[i] for i in example]\n", "\n", "\n", "def compute_metrics(p):\n", " predictions, labels = p\n", " predictions = np.argmax(predictions, axis=2)\n", "\n", " true_predictions = [\n", " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", " true_labels = [\n", " [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", "\n", " results = seqeval.compute(predictions=true_predictions, references=true_labels)\n", " return {\n", " \"precision\": results[\"overall_precision\"],\n", " \"recall\": results[\"overall_recall\"],\n", " \"f1\": results[\"overall_f1\"],\n", " \"accuracy\": results[\"overall_accuracy\"],\n", " }" ], "metadata": { "id": "ia5vLfKTvTjy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "id2label = {\n", " 0:\"O\",\n", " 1:\"B-ORG\",\n", " 2:\"I-ORG\",\n", " 3:\"B-GCNUMBER\",\n", " 4:\"I-GCNUMBER\",\n", " 5:\"B-BGNUMBER\",\n", " 6:\"I-BGNUMBER\",\n", " 7:\"B-NAME\",\n", " 8:\"I-NAME\",\n", " 9:\"B-LOCATION\",\n", " 10:\"I-LOCATION\",\n", " 11:\"B-COUNTRY\",\n", " 12:\"I-COUNTRY\",\n", "}\n", "label2id = {\n", " \"O\" :0 ,\n", " \"B-ORG\" :1 ,\n", " \"I-ORG\" :2 ,\n", " \"B-GCNUMBER\" :3 ,\n", " \"I-GCNUMBER\" :4 ,\n", " \"B-BGNUMBER\" :5 ,\n", " \"I-BGNUMBER\" :6 ,\n", " \"B-NAME\" :7 ,\n", " \"I-NAME\" :8 ,\n", " \"B-LOCATION\" :9 ,\n", " \"I-LOCATION\":10 ,\n", " \"B-COUNTRY\":11 ,\n", " \"I-COUNTRY\":12 ,\n", "}" ], "metadata": { "id": "1hkWx_1EvfQZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import AutoModelForTokenClassification\n", "model = AutoModelForTokenClassification.from_pretrained(\"bert-base-uncased\", num_labels=13,id2label=id2label, label2id=label2id)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105, "referenced_widgets": [ "d09bf4b2b00645ae801039fe86eec1be", "53dae5f5782446b79a54dfba95950859", "9c0f82538b15420f8810dd3c14b942f6", "bd7e5e72d86a4f3290a5c92b61fc8b3d", "4aef3db3496c4117b72884967266e992", "f12403295a384081bfa2748147d7b03d", "3bfef039f33245e795ce431dd20b58be", "1178a785cf1c468dbfcf4b4a8c622249", "3186a33bf7b04dd7a8b7368acee94c47", "397ea04a9a7b40ec8e7edf03a66b4fa0", "fdbeb8f01a1c46cba428648be4c1dd13" ] }, "id": "RjZmZ-ztxC2h", "outputId": "842a7cad-dbec-4e13-8d86-50ecf3916456" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/440M [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [210/210 08:28, Epoch 30/30]\n", "
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EpochTraining LossValidation LossPrecisionRecallF1Accuracy
1No log1.9826270.0000000.0000000.0000000.429379
2No log1.7524540.3076920.0533330.0909090.491525
3No log1.5701540.3846150.1333330.1980200.514124
4No log1.4392910.3666670.1466670.2095240.525424
5No log1.3169810.3939390.1733330.2407410.553672
6No log1.2043730.5833330.2800000.3783780.604520
7No log1.0846890.5961540.4133330.4881890.661017
8No log0.9798390.6500000.5200000.5777780.711864
9No log0.8890850.6857140.6400000.6620690.757062
10No log0.8046490.7183100.6800000.6986300.774011
11No log0.7313010.7297300.7200000.7248320.802260
12No log0.6669180.8157890.8266670.8211920.853107
13No log0.6452520.7948720.8266670.8104580.853107
14No log0.5993390.8076920.8400000.8235290.864407
15No log0.5591120.8205130.8533330.8366010.870056
16No log0.5497880.7804880.8533330.8152870.870056
17No log0.5298460.8000000.8533330.8258060.875706
18No log0.5213260.7777780.8400000.8076920.864407
19No log0.5234220.8250000.8800000.8516130.887006
20No log0.5238080.8227850.8666670.8441560.881356
21No log0.5359670.8170730.8933330.8535030.887006
22No log0.5156440.8250000.8800000.8516130.887006
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=210, training_loss=0.47932579403831843, metrics={'train_runtime': 508.6205, 'train_samples_per_second': 2.949, 'train_steps_per_second': 0.413, 'total_flos': 52770861370944.0, 'train_loss': 0.47932579403831843, 'epoch': 30.0})" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "markdown", "source": [ "#### push to hub the final model" ], "metadata": { "id": "ZWWdzstTQvVl" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 149, "referenced_widgets": [ "fba645b2f67747e6b6567ec0a5fdea20", "79547ecdb2684c93b670c9b654429402", "fd6624bacc6d4c47acae7b4ede8c1f3b", "6cd6ee458afb48d398022267f39b0fc4", "9b66439e9e7e422a97995cfb5d8f33b8", "9858a3b7c3944e4fae6b3fadc9642125", "775a0752d94a40138acdee0db8739031", "445f2113b9f7485c868570d49eb52568", "e709ba7a504b4efe934d3ee7774ffc5f", "42ebf1faa74b46d5b1b868f83f44a657", "b8992ac697014624a58a541efca24655", "be4b170b77c2428a83104f56c2774eff", "ad4b06c06e894c58956c8708c0ccfd67", "f3efe96ce3d14872b1b62ef0b0f6122f", "1a6cb5fe580141c6bdeb1dc0a321d380", "1f2a427df9604930b915441f2e89ddd3", "2ac0d35389794c3faceb679a43c7ceea", "b31ac4e6bd36460987d448b9e7b1f08c", "492b9b0c9f594ef6ae0d238b694ca181", "97f064e2b05f44778b84d5d5d9cad20e", "43e09ad9787244f8bf20c01c7da81991", "46b4241483ea4dc984cc92cce41514ee", "3afc5d399625437196c6a52d36373f61", "62f221b1ab7a4c608be46ab9fab43af1", "cbca91cd984d42fd9ad90fcb58331d6e", "63931ac1a75241dfb7519da2194969d2", "a576f7800c0740a1a7b70c55e2c0f071", "5cd48e3b8d1c42edadc2413f1baac426", "51efa950d7a44737b9e7dbdf177be9a3", "59878cd245324664bbeb4aefc0fc36e4", "bac939ffbe8f4edd9d4bf7f4281c0707", "71c6509f4497436fb9bd2c3e141990f4", "20c2b2fd5aa24683ad3cbe18a485bcb1" ] }, "id": "rsUb3czVK8ub", 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nvidia-cusolver-cu12, mpld3, bpemb, boto3, pytorch-revgrad, accelerate, transformer-smaller-training-vocab, flair\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 2.0.7\n", " Uninstalling urllib3-2.0.7:\n", " Successfully uninstalled urllib3-2.0.7\n", "Successfully installed accelerate-0.28.0 boto3-1.34.75 botocore-1.34.75 bpemb-0.3.5 conllu-4.5.3 deprecated-1.2.14 flair-0.13.1 ftfy-6.2.0 janome-0.5.0 jmespath-1.0.1 langdetect-1.0.9 mpld3-0.5.10 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pptree-3.1 pytorch-revgrad-0.2.0 s3transfer-0.10.1 segtok-1.5.11 semver-3.0.2 sqlitedict-2.1.0 transformer-smaller-training-vocab-0.3.3 urllib3-1.26.18 wikipedia-api-0.6.0\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install torch" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xZ9OVcoGJ5HI", "outputId": "3ba88ce4-6389-4bde-8b45-cd19b49c2adc" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.3)\n", "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.10.0)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n", "Requirement 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"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n" ] } ] }, { "cell_type": "code", "source": [ "# directory where the data resides\n", "from flair.datasets import ColumnCorpus\n", "from flair.data import Corpus\n", "data_folder = '/content'\n", "# initializing the corpus\n", "columns = {0 : 'text', 1 : 'ner'}\n", "corpus:Corpus = ColumnCorpus(data_folder, columns,\n", " train_file = 'train.txt',\n", " test_file = 'test.txt',\n", " dev_file = 'val.txt')" ], "metadata": { "id": "Kcdec6ATIWWO", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "38387105-26f7-49b3-84ae-1c838893eece" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:16,830 Reading data from /content\n", "2024-04-02 12:38:16,832 Train: /content/train.txt\n", "2024-04-02 12:38:16,833 Dev: /content/val.txt\n", "2024-04-02 12:38:16,834 Test: /content/test.txt\n" ] } ] }, { "cell_type": "code", "source": [ "# prompt: hstack merge 2 numpy array\n", "\n", "import numpy as np\n", "\n", "# Create two numpy arrays\n", "arr1 = np.array([1, 2, 3])\n", "arr2 = np.array([4, 5, 6])\n", "\n", "# Concatenate the arrays horizontally (side-by-side)\n", "arr_hstack = np.hstack((arr1, arr2))\n", "\n", "# Print the resulting array\n", "print(arr_hstack)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "poqwQIWP2p3R", "outputId": "9e3d838a-4ddd-4647-d0c3-531634922f38" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[1 2 3 4 5 6]\n" ] } ] }, { "cell_type": "code", "source": [ "from flair.data import Corpus\n", "from flair.datasets import ColumnCorpus\n", "from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings\n", "from flair.embeddings import TransformerWordEmbeddings\n", "\n", "# embeddings = TransformerWordEmbeddings(\n", "# model='distilbert-base-cased',\n", "# layers=\"-1\",\n", "# subtoken_pooling=\"first\",\n", "# fine_tune=True,\n", "# use_context=True,\n", "# )\n", "\n", "# 1. get the corpus\n", "columns = {0 : 'text', 1 : 'ner'}\n", "corpus:Corpus = ColumnCorpus(data_folder, columns,\n", " train_file = 'train.txt',\n", " test_file = 'test.txt',\n", " dev_file = 'val.txt')\n", "\n", "# 2. what tag do we want to predict?\n", "tag_type = 'ner'\n", "\n", "# 3. make the tag dictionary from the corpus\n", "tag_dictionary = corpus.make_label_dictionary(label_type = 'ner')\n", "print(tag_dictionary)\n", "# 4. initialize each embedding we use\n", "embedding_types = [\n", " WordEmbeddings('en-glove'),\n", " FlairEmbeddings('news-forward'),\n", " FlairEmbeddings('news-backward'),\n", "]\n", "\n", "# embedding stack consists of Flair and GloVe embeddings\n", "embeddings = StackedEmbeddings(embeddings=embedding_types)\n", "\n", "# 5. initialize sequence tagger\n", "from flair.models import SequenceTagger\n", "\n", "tagger = SequenceTagger(hidden_size=256,\n", " embeddings=embeddings,\n", " tag_dictionary=tag_dictionary,\n", " tag_type=tag_type)\n", "\n", "# 6. initialize trainer\n", "from flair.trainers import ModelTrainer\n", "\n", "trainer = ModelTrainer(tagger, corpus)\n", "\n", "# 7. run training\n", "trainer.train('/content/drive/MyDrive/resources/taggers/ner-english',\n", " train_with_dev=True,\n", " max_epochs=150)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wA0xkTtZoiBY", "outputId": "abcb9503-849f-40aa-9cbf-2cc67ee1f258" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:21,457 Reading data from /content\n", "2024-04-02 12:38:21,460 Train: /content/train.txt\n", "2024-04-02 12:38:21,463 Dev: /content/val.txt\n", "2024-04-02 12:38:21,468 Test: /content/test.txt\n", "2024-04-02 12:38:21,549 Computing label dictionary. Progress:\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "0it [00:00, ?it/s]\n", "45it [00:00, 15121.27it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:21,619 Dictionary created for label 'ner' with 2 values: GCNUM (seen 43 times), TRACK-ID (seen 42 times)\n", "Dictionary with 2 tags: GCNUM, TRACK-ID\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:21,903 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim.vectors.npy not found in cache, downloading to /tmp/tmpnvmwlbpa\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 153M/153M [00:04<00:00, 38.2MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:26,387 copying /tmp/tmpnvmwlbpa to cache at /root/.flair/embeddings/glove.gensim.vectors.npy\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:27,524 removing temp file /tmp/tmpnvmwlbpa\n", "2024-04-02 12:38:27,835 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim not found in cache, downloading to /tmp/tmpydg9ahnl\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 20.5M/20.5M [00:00<00:00, 30.1MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:28,743 copying /tmp/tmpydg9ahnl to cache at /root/.flair/embeddings/glove.gensim\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:28,820 removing temp file /tmp/tmpydg9ahnl\n", "2024-04-02 12:38:37,031 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-forward-0.4.1.pt not found in cache, downloading to /tmp/tmpeed_9gtd\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 69.7M/69.7M [00:01<00:00, 36.9MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:39,131 copying /tmp/tmpeed_9gtd to cache at /root/.flair/embeddings/news-forward-0.4.1.pt\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:39,229 removing temp file /tmp/tmpeed_9gtd\n", "2024-04-02 12:38:40,492 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-backward-0.4.1.pt not found in cache, downloading to /tmp/tmphmv3s30g\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 69.7M/69.7M [00:01<00:00, 36.9MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:42,596 copying /tmp/tmphmv3s30g to cache at /root/.flair/embeddings/news-backward-0.4.1.pt\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:38:42,755 removing temp file /tmp/tmphmv3s30g\n", "2024-04-02 12:38:43,154 SequenceTagger predicts: Dictionary with 9 tags: O, S-GCNUM, B-GCNUM, E-GCNUM, I-GCNUM, S-TRACK-ID, B-TRACK-ID, E-TRACK-ID, I-TRACK-ID\n", "2024-04-02 12:38:43,551 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,553 Model: \"SequenceTagger(\n", " (embeddings): StackedEmbeddings(\n", " (list_embedding_0): WordEmbeddings(\n", " 'en-glove'\n", " (embedding): Embedding(400001, 100)\n", " )\n", " (list_embedding_1): FlairEmbeddings(\n", " (lm): LanguageModel(\n", " (drop): Dropout(p=0.05, inplace=False)\n", " (encoder): Embedding(300, 100)\n", " (rnn): LSTM(100, 2048)\n", " )\n", " )\n", " (list_embedding_2): FlairEmbeddings(\n", " (lm): LanguageModel(\n", " (drop): Dropout(p=0.05, inplace=False)\n", " (encoder): Embedding(300, 100)\n", " (rnn): LSTM(100, 2048)\n", " )\n", " )\n", " )\n", " (word_dropout): WordDropout(p=0.05)\n", " (locked_dropout): LockedDropout(p=0.5)\n", " (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)\n", " (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)\n", " (linear): Linear(in_features=512, out_features=11, bias=True)\n", " (loss_function): ViterbiLoss()\n", " (crf): CRF()\n", ")\"\n", "2024-04-02 12:38:43,555 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,557 Corpus: 45 train + 16 dev + 3 test sentences\n", "2024-04-02 12:38:43,558 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,560 Train: 61 sentences\n", "2024-04-02 12:38:43,562 (train_with_dev=True, train_with_test=False)\n", "2024-04-02 12:38:43,563 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,564 Training Params:\n", "2024-04-02 12:38:43,565 - learning_rate: \"0.1\" \n", "2024-04-02 12:38:43,566 - mini_batch_size: \"32\"\n", "2024-04-02 12:38:43,567 - max_epochs: \"150\"\n", "2024-04-02 12:38:43,568 - shuffle: \"True\"\n", "2024-04-02 12:38:43,569 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,570 Plugins:\n", "2024-04-02 12:38:43,571 - AnnealOnPlateau | patience: '3', anneal_factor: '0.5', min_learning_rate: '0.0001'\n", "2024-04-02 12:38:43,572 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,573 Final evaluation on model from best epoch (best-model.pt)\n", "2024-04-02 12:38:43,574 - metric: \"('micro avg', 'f1-score')\"\n", "2024-04-02 12:38:43,575 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,576 Computation:\n", "2024-04-02 12:38:43,577 - compute on device: cuda:0\n", "2024-04-02 12:38:43,578 - embedding storage: cpu\n", "2024-04-02 12:38:43,578 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,579 Model training base path: \"/content/drive/MyDrive/resources/taggers/ner-english\"\n", "2024-04-02 12:38:43,580 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:43,581 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:44,976 epoch 1 - iter 1/2 - loss 3.44404515 - time (sec): 1.39 - samples/sec: 402.00 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,490 epoch 1 - iter 2/2 - loss 2.92139873 - time (sec): 1.91 - samples/sec: 569.80 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,492 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,494 EPOCH 1 done: loss 2.9214 - lr: 0.100000\n", "2024-04-02 12:38:45,496 - 0 epochs without improvement\n", "2024-04-02 12:38:45,498 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,597 epoch 2 - iter 1/2 - loss 1.28463274 - time (sec): 0.10 - samples/sec: 6158.11 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,672 epoch 2 - iter 2/2 - loss 1.07516005 - time (sec): 0.17 - samples/sec: 6349.58 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,673 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,676 EPOCH 2 done: loss 1.0752 - lr: 0.100000\n", "2024-04-02 12:38:45,677 - 0 epochs without improvement\n", "2024-04-02 12:38:45,679 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,753 epoch 3 - iter 1/2 - loss 0.69531815 - time (sec): 0.07 - samples/sec: 7510.14 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,845 epoch 3 - iter 2/2 - loss 0.70569185 - time (sec): 0.16 - samples/sec: 6627.54 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:45,847 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,850 EPOCH 3 done: loss 0.7057 - lr: 0.100000\n", "2024-04-02 12:38:45,852 - 0 epochs without improvement\n", "2024-04-02 12:38:45,854 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:45,948 epoch 4 - iter 1/2 - loss 0.65479236 - time (sec): 0.09 - samples/sec: 6255.63 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,022 epoch 4 - iter 2/2 - loss 0.67509532 - time (sec): 0.17 - samples/sec: 6535.21 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,024 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,027 EPOCH 4 done: loss 0.6751 - lr: 0.100000\n", "2024-04-02 12:38:46,030 - 0 epochs without improvement\n", "2024-04-02 12:38:46,032 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,131 epoch 5 - iter 1/2 - loss 0.64115141 - time (sec): 0.10 - samples/sec: 5984.08 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,206 epoch 5 - iter 2/2 - loss 0.64161874 - time (sec): 0.17 - samples/sec: 6370.44 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,207 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,210 EPOCH 5 done: loss 0.6416 - lr: 0.100000\n", "2024-04-02 12:38:46,212 - 0 epochs without improvement\n", "2024-04-02 12:38:46,215 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,310 epoch 6 - iter 1/2 - loss 0.58738084 - time (sec): 0.09 - samples/sec: 6258.22 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,385 epoch 6 - iter 2/2 - loss 0.59091217 - time (sec): 0.17 - samples/sec: 6477.29 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,387 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,389 EPOCH 6 done: loss 0.5909 - lr: 0.100000\n", "2024-04-02 12:38:46,391 - 0 epochs without improvement\n", "2024-04-02 12:38:46,393 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,480 epoch 7 - iter 1/2 - loss 0.55795262 - time (sec): 0.08 - samples/sec: 6800.72 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,571 epoch 7 - iter 2/2 - loss 0.58513366 - time (sec): 0.18 - samples/sec: 6187.30 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,573 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,575 EPOCH 7 done: loss 0.5851 - lr: 0.100000\n", "2024-04-02 12:38:46,578 - 0 epochs without improvement\n", "2024-04-02 12:38:46,580 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,664 epoch 8 - iter 1/2 - loss 0.50878422 - time (sec): 0.08 - samples/sec: 6799.65 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,754 epoch 8 - iter 2/2 - loss 0.54670745 - time (sec): 0.17 - samples/sec: 6304.28 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,757 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,759 EPOCH 8 done: loss 0.5467 - lr: 0.100000\n", "2024-04-02 12:38:46,762 - 0 epochs without improvement\n", "2024-04-02 12:38:46,764 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,862 epoch 9 - iter 1/2 - loss 0.42821300 - time (sec): 0.10 - samples/sec: 6373.38 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,933 epoch 9 - iter 2/2 - loss 0.54806942 - time (sec): 0.17 - samples/sec: 6544.42 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:46,935 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:46,937 EPOCH 9 done: loss 0.5481 - lr: 0.100000\n", "2024-04-02 12:38:46,940 - 1 epochs without improvement\n", "2024-04-02 12:38:46,942 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,021 epoch 10 - iter 1/2 - loss 0.47240739 - time (sec): 0.08 - samples/sec: 7317.90 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,110 epoch 10 - iter 2/2 - loss 0.50139522 - time (sec): 0.17 - samples/sec: 6507.94 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,112 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,115 EPOCH 10 done: loss 0.5014 - lr: 0.100000\n", "2024-04-02 12:38:47,117 - 0 epochs without improvement\n", "2024-04-02 12:38:47,119 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,219 epoch 11 - iter 1/2 - loss 0.44289096 - time (sec): 0.10 - samples/sec: 6106.63 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,297 epoch 11 - iter 2/2 - loss 0.47118983 - time (sec): 0.17 - samples/sec: 6227.87 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,299 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,301 EPOCH 11 done: loss 0.4712 - lr: 0.100000\n", "2024-04-02 12:38:47,303 - 0 epochs without improvement\n", "2024-04-02 12:38:47,306 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,405 epoch 12 - iter 1/2 - loss 0.42449305 - time (sec): 0.10 - samples/sec: 6091.96 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,510 epoch 12 - iter 2/2 - loss 0.45789551 - time (sec): 0.20 - samples/sec: 5407.34 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,512 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,514 EPOCH 12 done: loss 0.4579 - lr: 0.100000\n", "2024-04-02 12:38:47,516 - 0 epochs without improvement\n", "2024-04-02 12:38:47,518 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,596 epoch 13 - iter 1/2 - loss 0.44945066 - time (sec): 0.08 - samples/sec: 7236.94 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,692 epoch 13 - iter 2/2 - loss 0.44613633 - time (sec): 0.17 - samples/sec: 6291.01 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,694 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,696 EPOCH 13 done: loss 0.4461 - lr: 0.100000\n", "2024-04-02 12:38:47,698 - 0 epochs without improvement\n", "2024-04-02 12:38:47,700 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,779 epoch 14 - iter 1/2 - loss 0.38720482 - time (sec): 0.08 - samples/sec: 7137.44 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,873 epoch 14 - iter 2/2 - loss 0.42500368 - time (sec): 0.17 - samples/sec: 6353.72 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:47,874 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,877 EPOCH 14 done: loss 0.4250 - lr: 0.100000\n", "2024-04-02 12:38:47,879 - 0 epochs without improvement\n", "2024-04-02 12:38:47,881 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:47,979 epoch 15 - iter 1/2 - loss 0.41062502 - time (sec): 0.10 - samples/sec: 6093.96 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,056 epoch 15 - iter 2/2 - loss 0.42484871 - time (sec): 0.17 - samples/sec: 6298.04 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,057 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,059 EPOCH 15 done: loss 0.4248 - lr: 0.100000\n", "2024-04-02 12:38:48,061 - 0 epochs without improvement\n", "2024-04-02 12:38:48,063 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,164 epoch 16 - iter 1/2 - loss 0.42647916 - time (sec): 0.10 - samples/sec: 5824.25 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,243 epoch 16 - iter 2/2 - loss 0.41399557 - time (sec): 0.18 - samples/sec: 6146.45 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,245 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,247 EPOCH 16 done: loss 0.4140 - lr: 0.100000\n", "2024-04-02 12:38:48,250 - 0 epochs without improvement\n", "2024-04-02 12:38:48,252 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,334 epoch 17 - iter 1/2 - loss 0.39168397 - time (sec): 0.08 - samples/sec: 6976.05 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,427 epoch 17 - iter 2/2 - loss 0.39061826 - time (sec): 0.17 - samples/sec: 6309.59 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,428 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,431 EPOCH 17 done: loss 0.3906 - lr: 0.100000\n", "2024-04-02 12:38:48,434 - 0 epochs without improvement\n", "2024-04-02 12:38:48,436 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,527 epoch 18 - iter 1/2 - loss 0.40167976 - time (sec): 0.09 - samples/sec: 6566.29 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,619 epoch 18 - iter 2/2 - loss 0.37354796 - time (sec): 0.18 - samples/sec: 6027.18 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,621 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,623 EPOCH 18 done: loss 0.3735 - lr: 0.100000\n", "2024-04-02 12:38:48,626 - 0 epochs without improvement\n", "2024-04-02 12:38:48,628 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,729 epoch 19 - iter 1/2 - loss 0.35808818 - time (sec): 0.10 - samples/sec: 5792.09 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,810 epoch 19 - iter 2/2 - loss 0.36616759 - time (sec): 0.18 - samples/sec: 6064.52 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:48,812 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,815 EPOCH 19 done: loss 0.3662 - lr: 0.100000\n", "2024-04-02 12:38:48,817 - 0 epochs without improvement\n", "2024-04-02 12:38:48,820 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:48,921 epoch 20 - iter 1/2 - loss 0.34479054 - time (sec): 0.10 - samples/sec: 5643.94 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,003 epoch 20 - iter 2/2 - loss 0.34221306 - time (sec): 0.18 - samples/sec: 6013.25 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,004 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,008 EPOCH 20 done: loss 0.3422 - lr: 0.100000\n", "2024-04-02 12:38:49,012 - 0 epochs without improvement\n", "2024-04-02 12:38:49,014 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,112 epoch 21 - iter 1/2 - loss 0.32889740 - time (sec): 0.09 - samples/sec: 5996.51 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,192 epoch 21 - iter 2/2 - loss 0.33733788 - time (sec): 0.17 - samples/sec: 6216.19 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,195 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,198 EPOCH 21 done: loss 0.3373 - lr: 0.100000\n", "2024-04-02 12:38:49,203 - 0 epochs without improvement\n", "2024-04-02 12:38:49,208 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,306 epoch 22 - iter 1/2 - loss 0.27969826 - time (sec): 0.10 - samples/sec: 6196.75 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,386 epoch 22 - iter 2/2 - loss 0.32045580 - time (sec): 0.18 - samples/sec: 6186.06 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,388 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,390 EPOCH 22 done: loss 0.3205 - lr: 0.100000\n", "2024-04-02 12:38:49,392 - 0 epochs without improvement\n", "2024-04-02 12:38:49,395 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,504 epoch 23 - iter 1/2 - loss 0.30802871 - time (sec): 0.11 - samples/sec: 5234.59 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,586 epoch 23 - iter 2/2 - loss 0.32645577 - time (sec): 0.19 - samples/sec: 5728.11 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,589 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,591 EPOCH 23 done: loss 0.3265 - lr: 0.100000\n", "2024-04-02 12:38:49,593 - 1 epochs without improvement\n", "2024-04-02 12:38:49,595 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,678 epoch 24 - iter 1/2 - loss 0.29259292 - time (sec): 0.08 - samples/sec: 7146.77 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,772 epoch 24 - iter 2/2 - loss 0.28968734 - time (sec): 0.17 - samples/sec: 6245.59 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,773 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,776 EPOCH 24 done: loss 0.2897 - lr: 0.100000\n", "2024-04-02 12:38:49,778 - 0 epochs without improvement\n", "2024-04-02 12:38:49,781 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,882 epoch 25 - iter 1/2 - loss 0.26763737 - time (sec): 0.10 - samples/sec: 5888.06 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,964 epoch 25 - iter 2/2 - loss 0.30177488 - time (sec): 0.18 - samples/sec: 6028.73 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:49,965 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:49,968 EPOCH 25 done: loss 0.3018 - lr: 0.100000\n", "2024-04-02 12:38:49,971 - 1 epochs without improvement\n", "2024-04-02 12:38:49,973 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,070 epoch 26 - iter 1/2 - loss 0.26146290 - time (sec): 0.09 - samples/sec: 5978.86 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,149 epoch 26 - iter 2/2 - loss 0.27233112 - time (sec): 0.17 - samples/sec: 6300.31 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,150 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,154 EPOCH 26 done: loss 0.2723 - lr: 0.100000\n", "2024-04-02 12:38:50,156 - 0 epochs without improvement\n", "2024-04-02 12:38:50,158 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,255 epoch 27 - iter 1/2 - loss 0.24458568 - time (sec): 0.09 - samples/sec: 6267.43 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,341 epoch 27 - iter 2/2 - loss 0.26482273 - time (sec): 0.18 - samples/sec: 6030.94 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,343 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,345 EPOCH 27 done: loss 0.2648 - lr: 0.100000\n", "2024-04-02 12:38:50,347 - 0 epochs without improvement\n", "2024-04-02 12:38:50,350 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,432 epoch 28 - iter 1/2 - loss 0.25307725 - time (sec): 0.08 - samples/sec: 7351.31 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,538 epoch 28 - iter 2/2 - loss 0.25461486 - time (sec): 0.19 - samples/sec: 5841.93 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,540 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,542 EPOCH 28 done: loss 0.2546 - lr: 0.100000\n", "2024-04-02 12:38:50,544 - 0 epochs without improvement\n", "2024-04-02 12:38:50,546 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,626 epoch 29 - iter 1/2 - loss 0.26980714 - time (sec): 0.08 - samples/sec: 7002.56 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,725 epoch 29 - iter 2/2 - loss 0.24249902 - time (sec): 0.18 - samples/sec: 6163.12 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,727 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,729 EPOCH 29 done: loss 0.2425 - lr: 0.100000\n", "2024-04-02 12:38:50,731 - 0 epochs without improvement\n", "2024-04-02 12:38:50,733 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,814 epoch 30 - iter 1/2 - loss 0.19740648 - time (sec): 0.08 - samples/sec: 7302.75 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,909 epoch 30 - iter 2/2 - loss 0.23087483 - time (sec): 0.17 - samples/sec: 6274.14 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:50,910 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:50,913 EPOCH 30 done: loss 0.2309 - lr: 0.100000\n", "2024-04-02 12:38:50,915 - 0 epochs without improvement\n", "2024-04-02 12:38:50,917 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,013 epoch 31 - iter 1/2 - loss 0.23110126 - time (sec): 0.09 - samples/sec: 5975.11 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,100 epoch 31 - iter 2/2 - loss 0.22741171 - time (sec): 0.18 - samples/sec: 6028.79 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,102 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,104 EPOCH 31 done: loss 0.2274 - lr: 0.100000\n", "2024-04-02 12:38:51,106 - 0 epochs without improvement\n", "2024-04-02 12:38:51,108 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,211 epoch 32 - iter 1/2 - loss 0.24045745 - time (sec): 0.10 - samples/sec: 5865.59 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,292 epoch 32 - iter 2/2 - loss 0.25111941 - time (sec): 0.18 - samples/sec: 5985.53 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,293 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,295 EPOCH 32 done: loss 0.2511 - lr: 0.100000\n", "2024-04-02 12:38:51,297 - 1 epochs without improvement\n", "2024-04-02 12:38:51,299 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,400 epoch 33 - iter 1/2 - loss 0.21789198 - time (sec): 0.10 - samples/sec: 5989.39 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,480 epoch 33 - iter 2/2 - loss 0.22149147 - time (sec): 0.18 - samples/sec: 6080.23 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,482 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,484 EPOCH 33 done: loss 0.2215 - lr: 0.100000\n", "2024-04-02 12:38:51,486 - 0 epochs without improvement\n", "2024-04-02 12:38:51,488 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,595 epoch 34 - iter 1/2 - loss 0.23112546 - time (sec): 0.10 - samples/sec: 5508.79 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,682 epoch 34 - iter 2/2 - loss 0.22252127 - time (sec): 0.19 - samples/sec: 5682.85 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,684 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,687 EPOCH 34 done: loss 0.2225 - lr: 0.100000\n", "2024-04-02 12:38:51,689 - 1 epochs without improvement\n", "2024-04-02 12:38:51,691 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,770 epoch 35 - iter 1/2 - loss 0.21186577 - time (sec): 0.08 - samples/sec: 7131.70 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,871 epoch 35 - iter 2/2 - loss 0.21989569 - time (sec): 0.18 - samples/sec: 6117.52 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:51,873 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,876 EPOCH 35 done: loss 0.2199 - lr: 0.100000\n", "2024-04-02 12:38:51,878 - 0 epochs without improvement\n", "2024-04-02 12:38:51,880 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:51,962 epoch 36 - iter 1/2 - loss 0.24179878 - time (sec): 0.08 - samples/sec: 7033.68 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,059 epoch 36 - iter 2/2 - loss 0.20401711 - time (sec): 0.18 - samples/sec: 6140.66 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,061 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,063 EPOCH 36 done: loss 0.2040 - lr: 0.100000\n", "2024-04-02 12:38:52,065 - 0 epochs without improvement\n", "2024-04-02 12:38:52,068 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,150 epoch 37 - iter 1/2 - loss 0.20632705 - time (sec): 0.08 - samples/sec: 6811.88 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,248 epoch 37 - iter 2/2 - loss 0.21099051 - time (sec): 0.18 - samples/sec: 6095.13 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,250 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,253 EPOCH 37 done: loss 0.2110 - lr: 0.100000\n", "2024-04-02 12:38:52,255 - 1 epochs without improvement\n", "2024-04-02 12:38:52,257 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,358 epoch 38 - iter 1/2 - loss 0.18219516 - time (sec): 0.10 - samples/sec: 6054.16 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,444 epoch 38 - iter 2/2 - loss 0.20445531 - time (sec): 0.18 - samples/sec: 5895.79 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,447 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,449 EPOCH 38 done: loss 0.2045 - lr: 0.100000\n", "2024-04-02 12:38:52,451 - 2 epochs without improvement\n", "2024-04-02 12:38:52,453 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,561 epoch 39 - iter 1/2 - loss 0.22816141 - time (sec): 0.11 - samples/sec: 5549.30 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,651 epoch 39 - iter 2/2 - loss 0.19591870 - time (sec): 0.20 - samples/sec: 5542.28 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,653 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,655 EPOCH 39 done: loss 0.1959 - lr: 0.100000\n", "2024-04-02 12:38:52,658 - 0 epochs without improvement\n", "2024-04-02 12:38:52,664 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,739 epoch 40 - iter 1/2 - loss 0.20606261 - time (sec): 0.07 - samples/sec: 7483.91 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,836 epoch 40 - iter 2/2 - loss 0.19719533 - time (sec): 0.17 - samples/sec: 6357.67 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:52,838 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,840 EPOCH 40 done: loss 0.1972 - lr: 0.100000\n", "2024-04-02 12:38:52,842 - 1 epochs without improvement\n", "2024-04-02 12:38:52,844 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:52,946 epoch 41 - iter 1/2 - loss 0.19984477 - time (sec): 0.10 - samples/sec: 5879.62 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,025 epoch 41 - iter 2/2 - loss 0.20566837 - time (sec): 0.18 - samples/sec: 6056.58 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,027 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,031 EPOCH 41 done: loss 0.2057 - lr: 0.100000\n", "2024-04-02 12:38:53,034 - 2 epochs without improvement\n", "2024-04-02 12:38:53,036 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,136 epoch 42 - iter 1/2 - loss 0.18924362 - time (sec): 0.10 - samples/sec: 5850.44 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,215 epoch 42 - iter 2/2 - loss 0.20212508 - time (sec): 0.18 - samples/sec: 6141.99 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,217 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,219 EPOCH 42 done: loss 0.2021 - lr: 0.100000\n", "2024-04-02 12:38:53,222 - 3 epochs without improvement\n", "2024-04-02 12:38:53,224 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,306 epoch 43 - iter 1/2 - loss 0.20068359 - time (sec): 0.08 - samples/sec: 7153.91 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,401 epoch 43 - iter 2/2 - loss 0.17652243 - time (sec): 0.18 - samples/sec: 6209.30 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,403 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,405 EPOCH 43 done: loss 0.1765 - lr: 0.100000\n", "2024-04-02 12:38:53,407 - 0 epochs without improvement\n", "2024-04-02 12:38:53,410 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,488 epoch 44 - iter 1/2 - loss 0.15478516 - time (sec): 0.08 - samples/sec: 7239.23 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,595 epoch 44 - iter 2/2 - loss 0.17919922 - time (sec): 0.18 - samples/sec: 5938.84 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,597 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,599 EPOCH 44 done: loss 0.1792 - lr: 0.100000\n", "2024-04-02 12:38:53,600 - 1 epochs without improvement\n", "2024-04-02 12:38:53,602 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,685 epoch 45 - iter 1/2 - loss 0.17908520 - time (sec): 0.08 - samples/sec: 6931.02 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,785 epoch 45 - iter 2/2 - loss 0.18064003 - time (sec): 0.18 - samples/sec: 6005.46 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,788 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,790 EPOCH 45 done: loss 0.1806 - lr: 0.100000\n", "2024-04-02 12:38:53,795 - 2 epochs without improvement\n", "2024-04-02 12:38:53,797 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,902 epoch 46 - iter 1/2 - loss 0.15840863 - time (sec): 0.10 - samples/sec: 6008.01 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,980 epoch 46 - iter 2/2 - loss 0.18372312 - time (sec): 0.18 - samples/sec: 6019.85 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:53,982 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:53,984 EPOCH 46 done: loss 0.1837 - lr: 0.100000\n", "2024-04-02 12:38:53,986 - 3 epochs without improvement\n", "2024-04-02 12:38:53,988 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,088 epoch 47 - iter 1/2 - loss 0.17463030 - time (sec): 0.10 - samples/sec: 6164.60 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,167 epoch 47 - iter 2/2 - loss 0.17576261 - time (sec): 0.18 - samples/sec: 6179.81 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,168 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,170 EPOCH 47 done: loss 0.1758 - lr: 0.100000\n", "2024-04-02 12:38:54,173 - 0 epochs without improvement\n", "2024-04-02 12:38:54,175 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,258 epoch 48 - iter 1/2 - loss 0.13366355 - time (sec): 0.08 - samples/sec: 7025.57 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,353 epoch 48 - iter 2/2 - loss 0.17624550 - time (sec): 0.18 - samples/sec: 6159.30 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,355 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,357 EPOCH 48 done: loss 0.1762 - lr: 0.100000\n", "2024-04-02 12:38:54,359 - 1 epochs without improvement\n", "2024-04-02 12:38:54,361 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,466 epoch 49 - iter 1/2 - loss 0.15727480 - time (sec): 0.10 - samples/sec: 5638.24 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,574 epoch 49 - iter 2/2 - loss 0.17193385 - time (sec): 0.21 - samples/sec: 5155.73 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,579 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,580 EPOCH 49 done: loss 0.1719 - lr: 0.100000\n", "2024-04-02 12:38:54,581 - 0 epochs without improvement\n", "2024-04-02 12:38:54,583 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,730 epoch 50 - iter 1/2 - loss 0.14977512 - time (sec): 0.15 - samples/sec: 3910.86 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,844 epoch 50 - iter 2/2 - loss 0.15545896 - time (sec): 0.26 - samples/sec: 4200.34 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:54,849 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:54,853 EPOCH 50 done: loss 0.1555 - lr: 0.100000\n", "2024-04-02 12:38:54,854 - 0 epochs without improvement\n", "2024-04-02 12:38:54,859 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,000 epoch 51 - iter 1/2 - loss 0.15834969 - time (sec): 0.14 - samples/sec: 4268.16 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,096 epoch 51 - iter 2/2 - loss 0.15354020 - time (sec): 0.23 - samples/sec: 4634.39 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,098 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,099 EPOCH 51 done: loss 0.1535 - lr: 0.100000\n", "2024-04-02 12:38:55,102 - 0 epochs without improvement\n", "2024-04-02 12:38:55,104 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,235 epoch 52 - iter 1/2 - loss 0.14571885 - time (sec): 0.13 - samples/sec: 4605.82 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,340 epoch 52 - iter 2/2 - loss 0.15986135 - time (sec): 0.23 - samples/sec: 4671.90 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,342 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,344 EPOCH 52 done: loss 0.1599 - lr: 0.100000\n", "2024-04-02 12:38:55,346 - 1 epochs without improvement\n", "2024-04-02 12:38:55,348 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,480 epoch 53 - iter 1/2 - loss 0.10458194 - time (sec): 0.13 - samples/sec: 4550.62 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,582 epoch 53 - iter 2/2 - loss 0.12304710 - time (sec): 0.23 - samples/sec: 4678.70 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,585 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,586 EPOCH 53 done: loss 0.1230 - lr: 0.100000\n", "2024-04-02 12:38:55,588 - 0 epochs without improvement\n", "2024-04-02 12:38:55,590 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,713 epoch 54 - iter 1/2 - loss 0.16139668 - time (sec): 0.12 - samples/sec: 4894.67 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,851 epoch 54 - iter 2/2 - loss 0.14513430 - time (sec): 0.26 - samples/sec: 4198.29 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:55,853 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,855 EPOCH 54 done: loss 0.1451 - lr: 0.100000\n", "2024-04-02 12:38:55,858 - 1 epochs without improvement\n", "2024-04-02 12:38:55,860 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:55,965 epoch 55 - iter 1/2 - loss 0.18373649 - time (sec): 0.10 - samples/sec: 5480.08 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,107 epoch 55 - iter 2/2 - loss 0.16968290 - time (sec): 0.24 - samples/sec: 4449.06 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,109 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,111 EPOCH 55 done: loss 0.1697 - lr: 0.100000\n", "2024-04-02 12:38:56,114 - 2 epochs without improvement\n", "2024-04-02 12:38:56,116 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,269 epoch 56 - iter 1/2 - loss 0.14895640 - time (sec): 0.15 - samples/sec: 3905.18 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,380 epoch 56 - iter 2/2 - loss 0.14475585 - time (sec): 0.26 - samples/sec: 4139.29 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,383 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,385 EPOCH 56 done: loss 0.1448 - lr: 0.100000\n", "2024-04-02 12:38:56,387 - 3 epochs without improvement\n", "2024-04-02 12:38:56,389 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,504 epoch 57 - iter 1/2 - loss 0.15683315 - time (sec): 0.11 - samples/sec: 4974.73 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,609 epoch 57 - iter 2/2 - loss 0.14270435 - time (sec): 0.22 - samples/sec: 4993.55 - lr: 0.100000 - momentum: 0.000000\n", "2024-04-02 12:38:56,610 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,613 EPOCH 57 done: loss 0.1427 - lr: 0.100000\n", "2024-04-02 12:38:56,616 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.05]\n", "2024-04-02 12:38:56,617 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,734 epoch 58 - iter 1/2 - loss 0.14036764 - time (sec): 0.11 - samples/sec: 4821.27 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:56,824 epoch 58 - iter 2/2 - loss 0.12033280 - time (sec): 0.21 - samples/sec: 5295.05 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:56,826 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,828 EPOCH 58 done: loss 0.1203 - lr: 0.050000\n", "2024-04-02 12:38:56,830 - 0 epochs without improvement\n", "2024-04-02 12:38:56,832 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:56,917 epoch 59 - iter 1/2 - loss 0.11941228 - time (sec): 0.08 - samples/sec: 6737.79 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,016 epoch 59 - iter 2/2 - loss 0.11664262 - time (sec): 0.18 - samples/sec: 5949.76 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,018 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,020 EPOCH 59 done: loss 0.1166 - lr: 0.050000\n", "2024-04-02 12:38:57,022 - 0 epochs without improvement\n", "2024-04-02 12:38:57,024 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,109 epoch 60 - iter 1/2 - loss 0.09714617 - time (sec): 0.08 - samples/sec: 6770.29 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,211 epoch 60 - iter 2/2 - loss 0.11093283 - time (sec): 0.18 - samples/sec: 5897.83 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,213 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,215 EPOCH 60 done: loss 0.1109 - lr: 0.050000\n", "2024-04-02 12:38:57,217 - 0 epochs without improvement\n", "2024-04-02 12:38:57,218 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,325 epoch 61 - iter 1/2 - loss 0.11121512 - time (sec): 0.11 - samples/sec: 5487.88 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,409 epoch 61 - iter 2/2 - loss 0.11645126 - time (sec): 0.19 - samples/sec: 5730.14 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,411 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,413 EPOCH 61 done: loss 0.1165 - lr: 0.050000\n", "2024-04-02 12:38:57,415 - 1 epochs without improvement\n", "2024-04-02 12:38:57,417 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,500 epoch 62 - iter 1/2 - loss 0.13044009 - time (sec): 0.08 - samples/sec: 6848.41 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,598 epoch 62 - iter 2/2 - loss 0.11779617 - time (sec): 0.18 - samples/sec: 6077.83 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,600 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,602 EPOCH 62 done: loss 0.1178 - lr: 0.050000\n", "2024-04-02 12:38:57,603 - 2 epochs without improvement\n", "2024-04-02 12:38:57,605 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,707 epoch 63 - iter 1/2 - loss 0.10047390 - time (sec): 0.10 - samples/sec: 5865.93 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,794 epoch 63 - iter 2/2 - loss 0.11731148 - time (sec): 0.19 - samples/sec: 5841.52 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,795 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,798 EPOCH 63 done: loss 0.1173 - lr: 0.050000\n", "2024-04-02 12:38:57,801 - 3 epochs without improvement\n", "2024-04-02 12:38:57,805 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,880 epoch 64 - iter 1/2 - loss 0.10613811 - time (sec): 0.07 - samples/sec: 7459.84 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,978 epoch 64 - iter 2/2 - loss 0.10944687 - time (sec): 0.17 - samples/sec: 6387.11 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:57,979 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:57,982 EPOCH 64 done: loss 0.1094 - lr: 0.050000\n", "2024-04-02 12:38:57,984 - 0 epochs without improvement\n", "2024-04-02 12:38:57,986 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,069 epoch 65 - iter 1/2 - loss 0.13350388 - time (sec): 0.08 - samples/sec: 7159.80 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,167 epoch 65 - iter 2/2 - loss 0.10426040 - time (sec): 0.18 - samples/sec: 6064.69 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,169 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,171 EPOCH 65 done: loss 0.1043 - lr: 0.050000\n", "2024-04-02 12:38:58,173 - 0 epochs without improvement\n", "2024-04-02 12:38:58,176 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,254 epoch 66 - iter 1/2 - loss 0.07423303 - time (sec): 0.08 - samples/sec: 7207.75 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,350 epoch 66 - iter 2/2 - loss 0.09263823 - time (sec): 0.17 - samples/sec: 6322.38 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,351 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,354 EPOCH 66 done: loss 0.0926 - lr: 0.050000\n", "2024-04-02 12:38:58,356 - 0 epochs without improvement\n", "2024-04-02 12:38:58,358 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,462 epoch 67 - iter 1/2 - loss 0.08090147 - time (sec): 0.10 - samples/sec: 5731.48 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,545 epoch 67 - iter 2/2 - loss 0.09237679 - time (sec): 0.18 - samples/sec: 5896.55 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,546 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,549 EPOCH 67 done: loss 0.0924 - lr: 0.050000\n", "2024-04-02 12:38:58,551 - 0 epochs without improvement\n", "2024-04-02 12:38:58,553 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,653 epoch 68 - iter 1/2 - loss 0.09407286 - time (sec): 0.10 - samples/sec: 6222.91 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,731 epoch 68 - iter 2/2 - loss 0.10339344 - time (sec): 0.18 - samples/sec: 6188.13 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,733 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,735 EPOCH 68 done: loss 0.1034 - lr: 0.050000\n", "2024-04-02 12:38:58,737 - 1 epochs without improvement\n", "2024-04-02 12:38:58,740 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,850 epoch 69 - iter 1/2 - loss 0.09276591 - time (sec): 0.11 - samples/sec: 5410.58 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,929 epoch 69 - iter 2/2 - loss 0.08267720 - time (sec): 0.19 - samples/sec: 5808.62 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:58,930 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:58,933 EPOCH 69 done: loss 0.0827 - lr: 0.050000\n", "2024-04-02 12:38:58,936 - 0 epochs without improvement\n", "2024-04-02 12:38:58,938 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,040 epoch 70 - iter 1/2 - loss 0.10548705 - time (sec): 0.10 - samples/sec: 5882.08 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,121 epoch 70 - iter 2/2 - loss 0.09925675 - time (sec): 0.18 - samples/sec: 6022.49 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,123 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,125 EPOCH 70 done: loss 0.0993 - lr: 0.050000\n", "2024-04-02 12:38:59,128 - 1 epochs without improvement\n", "2024-04-02 12:38:59,130 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,210 epoch 71 - iter 1/2 - loss 0.11336365 - time (sec): 0.08 - samples/sec: 7135.39 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,318 epoch 71 - iter 2/2 - loss 0.09933357 - time (sec): 0.19 - samples/sec: 5869.40 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,320 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,321 EPOCH 71 done: loss 0.0993 - lr: 0.050000\n", "2024-04-02 12:38:59,323 - 2 epochs without improvement\n", "2024-04-02 12:38:59,325 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,430 epoch 72 - iter 1/2 - loss 0.10677456 - time (sec): 0.10 - samples/sec: 5976.21 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,504 epoch 72 - iter 2/2 - loss 0.10004914 - time (sec): 0.18 - samples/sec: 6152.94 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,506 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,508 EPOCH 72 done: loss 0.1000 - lr: 0.050000\n", "2024-04-02 12:38:59,510 - 3 epochs without improvement\n", "2024-04-02 12:38:59,512 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,615 epoch 73 - iter 1/2 - loss 0.08836593 - time (sec): 0.10 - samples/sec: 6013.71 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,702 epoch 73 - iter 2/2 - loss 0.08963128 - time (sec): 0.19 - samples/sec: 5798.26 - lr: 0.050000 - momentum: 0.000000\n", "2024-04-02 12:38:59,704 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,706 EPOCH 73 done: loss 0.0896 - lr: 0.050000\n", "2024-04-02 12:38:59,708 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.025]\n", "2024-04-02 12:38:59,710 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,800 epoch 74 - iter 1/2 - loss 0.07189532 - time (sec): 0.09 - samples/sec: 6479.90 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:38:59,897 epoch 74 - iter 2/2 - loss 0.08220644 - time (sec): 0.18 - samples/sec: 5903.60 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:38:59,899 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:38:59,901 EPOCH 74 done: loss 0.0822 - lr: 0.025000\n", "2024-04-02 12:38:59,903 - 0 epochs without improvement\n", "2024-04-02 12:38:59,905 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,007 epoch 75 - iter 1/2 - loss 0.08889680 - time (sec): 0.10 - samples/sec: 6085.51 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,087 epoch 75 - iter 2/2 - loss 0.07784245 - time (sec): 0.18 - samples/sec: 6033.48 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,089 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,091 EPOCH 75 done: loss 0.0778 - lr: 0.025000\n", "2024-04-02 12:39:00,093 - 0 epochs without improvement\n", "2024-04-02 12:39:00,095 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,178 epoch 76 - iter 1/2 - loss 0.08413783 - time (sec): 0.08 - samples/sec: 6942.49 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,277 epoch 76 - iter 2/2 - loss 0.07547427 - time (sec): 0.18 - samples/sec: 6040.45 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,278 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,281 EPOCH 76 done: loss 0.0755 - lr: 0.025000\n", "2024-04-02 12:39:00,283 - 0 epochs without improvement\n", "2024-04-02 12:39:00,285 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,369 epoch 77 - iter 1/2 - loss 0.08878959 - time (sec): 0.08 - samples/sec: 6850.04 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,467 epoch 77 - iter 2/2 - loss 0.08625149 - time (sec): 0.18 - samples/sec: 6048.16 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,469 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,471 EPOCH 77 done: loss 0.0863 - lr: 0.025000\n", "2024-04-02 12:39:00,474 - 1 epochs without improvement\n", "2024-04-02 12:39:00,476 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,587 epoch 78 - iter 1/2 - loss 0.07817116 - time (sec): 0.11 - samples/sec: 5550.93 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,670 epoch 78 - iter 2/2 - loss 0.08518015 - time (sec): 0.19 - samples/sec: 5658.80 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,672 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,674 EPOCH 78 done: loss 0.0852 - lr: 0.025000\n", "2024-04-02 12:39:00,676 - 2 epochs without improvement\n", "2024-04-02 12:39:00,678 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,777 epoch 79 - iter 1/2 - loss 0.07011843 - time (sec): 0.10 - samples/sec: 5666.35 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,871 epoch 79 - iter 2/2 - loss 0.08749443 - time (sec): 0.19 - samples/sec: 5668.71 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:00,873 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,875 EPOCH 79 done: loss 0.0875 - lr: 0.025000\n", "2024-04-02 12:39:00,878 - 3 epochs without improvement\n", "2024-04-02 12:39:00,880 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:00,982 epoch 80 - iter 1/2 - loss 0.06268984 - time (sec): 0.10 - samples/sec: 5884.95 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:01,062 epoch 80 - iter 2/2 - loss 0.08230347 - time (sec): 0.18 - samples/sec: 6049.08 - lr: 0.025000 - momentum: 0.000000\n", "2024-04-02 12:39:01,063 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,066 EPOCH 80 done: loss 0.0823 - lr: 0.025000\n", "2024-04-02 12:39:01,068 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0125]\n", "2024-04-02 12:39:01,070 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,174 epoch 81 - iter 1/2 - loss 0.07875583 - time (sec): 0.10 - samples/sec: 5871.13 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,254 epoch 81 - iter 2/2 - loss 0.06995134 - time (sec): 0.18 - samples/sec: 5988.31 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,255 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,258 EPOCH 81 done: loss 0.0700 - lr: 0.012500\n", "2024-04-02 12:39:01,260 - 0 epochs without improvement\n", "2024-04-02 12:39:01,262 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,345 epoch 82 - iter 1/2 - loss 0.07585760 - time (sec): 0.08 - samples/sec: 6868.62 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,443 epoch 82 - iter 2/2 - loss 0.06974426 - time (sec): 0.18 - samples/sec: 6097.87 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,445 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,448 EPOCH 82 done: loss 0.0697 - lr: 0.012500\n", "2024-04-02 12:39:01,450 - 0 epochs without improvement\n", "2024-04-02 12:39:01,452 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,554 epoch 83 - iter 1/2 - loss 0.07174847 - time (sec): 0.10 - samples/sec: 5756.80 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,637 epoch 83 - iter 2/2 - loss 0.07034619 - time (sec): 0.18 - samples/sec: 5967.24 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,639 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,641 EPOCH 83 done: loss 0.0703 - lr: 0.012500\n", "2024-04-02 12:39:01,644 - 1 epochs without improvement\n", "2024-04-02 12:39:01,646 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,747 epoch 84 - iter 1/2 - loss 0.06419448 - time (sec): 0.10 - samples/sec: 5921.64 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,836 epoch 84 - iter 2/2 - loss 0.07776609 - time (sec): 0.19 - samples/sec: 5767.26 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:01,839 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,841 EPOCH 84 done: loss 0.0778 - lr: 0.012500\n", "2024-04-02 12:39:01,843 - 2 epochs without improvement\n", "2024-04-02 12:39:01,846 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:01,952 epoch 85 - iter 1/2 - loss 0.09023521 - time (sec): 0.10 - samples/sec: 5571.57 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:02,034 epoch 85 - iter 2/2 - loss 0.08321849 - time (sec): 0.19 - samples/sec: 5813.13 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:02,036 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,038 EPOCH 85 done: loss 0.0832 - lr: 0.012500\n", "2024-04-02 12:39:02,040 - 3 epochs without improvement\n", "2024-04-02 12:39:02,042 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,122 epoch 86 - iter 1/2 - loss 0.05939736 - time (sec): 0.08 - samples/sec: 7163.40 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:02,219 epoch 86 - iter 2/2 - loss 0.07049263 - time (sec): 0.17 - samples/sec: 6216.52 - lr: 0.012500 - momentum: 0.000000\n", "2024-04-02 12:39:02,221 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,223 EPOCH 86 done: loss 0.0705 - lr: 0.012500\n", "2024-04-02 12:39:02,225 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00625]\n", "2024-04-02 12:39:02,227 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,313 epoch 87 - iter 1/2 - loss 0.09161505 - time (sec): 0.08 - samples/sec: 6758.79 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,411 epoch 87 - iter 2/2 - loss 0.07844079 - time (sec): 0.18 - samples/sec: 5980.73 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,412 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,414 EPOCH 87 done: loss 0.0784 - lr: 0.006250\n", "2024-04-02 12:39:02,416 - 1 epochs without improvement\n", "2024-04-02 12:39:02,419 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,503 epoch 88 - iter 1/2 - loss 0.06252489 - time (sec): 0.08 - samples/sec: 6884.54 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,599 epoch 88 - iter 2/2 - loss 0.05931741 - time (sec): 0.18 - samples/sec: 6098.92 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,600 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,603 EPOCH 88 done: loss 0.0593 - lr: 0.006250\n", "2024-04-02 12:39:02,605 - 0 epochs without improvement\n", "2024-04-02 12:39:02,610 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,717 epoch 89 - iter 1/2 - loss 0.06447771 - time (sec): 0.10 - samples/sec: 5676.90 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,799 epoch 89 - iter 2/2 - loss 0.07432611 - time (sec): 0.18 - samples/sec: 5928.24 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,801 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,806 EPOCH 89 done: loss 0.0743 - lr: 0.006250\n", "2024-04-02 12:39:02,809 - 1 epochs without improvement\n", "2024-04-02 12:39:02,811 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:02,917 epoch 90 - iter 1/2 - loss 0.06415097 - time (sec): 0.10 - samples/sec: 5681.39 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:02,998 epoch 90 - iter 2/2 - loss 0.06999941 - time (sec): 0.18 - samples/sec: 5895.46 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:03,000 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,002 EPOCH 90 done: loss 0.0700 - lr: 0.006250\n", "2024-04-02 12:39:03,004 - 2 epochs without improvement\n", "2024-04-02 12:39:03,006 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,107 epoch 91 - iter 1/2 - loss 0.07426291 - time (sec): 0.10 - samples/sec: 5820.25 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:03,191 epoch 91 - iter 2/2 - loss 0.08922745 - time (sec): 0.18 - samples/sec: 5930.01 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:03,194 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,196 EPOCH 91 done: loss 0.0892 - lr: 0.006250\n", "2024-04-02 12:39:03,198 - 3 epochs without improvement\n", "2024-04-02 12:39:03,199 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,294 epoch 92 - iter 1/2 - loss 0.08202867 - time (sec): 0.09 - samples/sec: 6106.90 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:03,391 epoch 92 - iter 2/2 - loss 0.06541127 - time (sec): 0.19 - samples/sec: 5713.71 - lr: 0.006250 - momentum: 0.000000\n", "2024-04-02 12:39:03,393 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,397 EPOCH 92 done: loss 0.0654 - lr: 0.006250\n", "2024-04-02 12:39:03,399 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.003125]\n", "2024-04-02 12:39:03,400 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,484 epoch 93 - iter 1/2 - loss 0.07317067 - time (sec): 0.08 - samples/sec: 6754.21 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,581 epoch 93 - iter 2/2 - loss 0.07066782 - time (sec): 0.18 - samples/sec: 6072.35 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,583 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,585 EPOCH 93 done: loss 0.0707 - lr: 0.003125\n", "2024-04-02 12:39:03,587 - 1 epochs without improvement\n", "2024-04-02 12:39:03,589 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,673 epoch 94 - iter 1/2 - loss 0.09488721 - time (sec): 0.08 - samples/sec: 6672.02 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,773 epoch 94 - iter 2/2 - loss 0.07745569 - time (sec): 0.18 - samples/sec: 6034.86 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,775 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,778 EPOCH 94 done: loss 0.0775 - lr: 0.003125\n", "2024-04-02 12:39:03,780 - 2 epochs without improvement\n", "2024-04-02 12:39:03,783 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,902 epoch 95 - iter 1/2 - loss 0.06722196 - time (sec): 0.12 - samples/sec: 5138.63 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,984 epoch 95 - iter 2/2 - loss 0.06787020 - time (sec): 0.20 - samples/sec: 5488.23 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:03,985 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:03,988 EPOCH 95 done: loss 0.0679 - lr: 0.003125\n", "2024-04-02 12:39:03,990 - 3 epochs without improvement\n", "2024-04-02 12:39:03,992 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,077 epoch 96 - iter 1/2 - loss 0.07972961 - time (sec): 0.08 - samples/sec: 6759.32 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:04,177 epoch 96 - iter 2/2 - loss 0.08482842 - time (sec): 0.18 - samples/sec: 5979.22 - lr: 0.003125 - momentum: 0.000000\n", "2024-04-02 12:39:04,179 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,182 EPOCH 96 done: loss 0.0848 - lr: 0.003125\n", "2024-04-02 12:39:04,184 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0015625]\n", "2024-04-02 12:39:04,187 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,274 epoch 97 - iter 1/2 - loss 0.07143414 - time (sec): 0.08 - samples/sec: 7129.24 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,369 epoch 97 - iter 2/2 - loss 0.06959602 - time (sec): 0.18 - samples/sec: 6103.03 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,371 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,373 EPOCH 97 done: loss 0.0696 - lr: 0.001563\n", "2024-04-02 12:39:04,375 - 1 epochs without improvement\n", "2024-04-02 12:39:04,378 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,461 epoch 98 - iter 1/2 - loss 0.05324973 - time (sec): 0.08 - samples/sec: 6916.43 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,558 epoch 98 - iter 2/2 - loss 0.06146819 - time (sec): 0.18 - samples/sec: 6102.94 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,560 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,562 EPOCH 98 done: loss 0.0615 - lr: 0.001563\n", "2024-04-02 12:39:04,565 - 2 epochs without improvement\n", "2024-04-02 12:39:04,567 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,670 epoch 99 - iter 1/2 - loss 0.05931310 - time (sec): 0.10 - samples/sec: 5753.78 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,754 epoch 99 - iter 2/2 - loss 0.07862271 - time (sec): 0.18 - samples/sec: 5893.28 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,755 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,758 EPOCH 99 done: loss 0.0786 - lr: 0.001563\n", "2024-04-02 12:39:04,761 - 3 epochs without improvement\n", "2024-04-02 12:39:04,763 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,865 epoch 100 - iter 1/2 - loss 0.05068619 - time (sec): 0.10 - samples/sec: 5671.71 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,955 epoch 100 - iter 2/2 - loss 0.06862395 - time (sec): 0.19 - samples/sec: 5727.15 - lr: 0.001563 - momentum: 0.000000\n", "2024-04-02 12:39:04,957 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:04,959 EPOCH 100 done: loss 0.0686 - lr: 0.001563\n", "2024-04-02 12:39:04,961 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00078125]\n", "2024-04-02 12:39:04,963 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,065 epoch 101 - iter 1/2 - loss 0.04959086 - time (sec): 0.10 - samples/sec: 5946.08 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,149 epoch 101 - iter 2/2 - loss 0.05854479 - time (sec): 0.18 - samples/sec: 5913.49 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,150 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,153 EPOCH 101 done: loss 0.0585 - lr: 0.000781\n", "2024-04-02 12:39:05,156 - 0 epochs without improvement\n", "2024-04-02 12:39:05,158 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,260 epoch 102 - iter 1/2 - loss 0.07184639 - time (sec): 0.10 - samples/sec: 5999.68 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,343 epoch 102 - iter 2/2 - loss 0.07709004 - time (sec): 0.18 - samples/sec: 5958.24 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,345 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,347 EPOCH 102 done: loss 0.0771 - lr: 0.000781\n", "2024-04-02 12:39:05,349 - 1 epochs without improvement\n", "2024-04-02 12:39:05,351 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,430 epoch 103 - iter 1/2 - loss 0.06619445 - time (sec): 0.08 - samples/sec: 7203.90 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,527 epoch 103 - iter 2/2 - loss 0.07354270 - time (sec): 0.17 - samples/sec: 6257.03 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,529 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,534 EPOCH 103 done: loss 0.0735 - lr: 0.000781\n", "2024-04-02 12:39:05,535 - 2 epochs without improvement\n", "2024-04-02 12:39:05,537 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,622 epoch 104 - iter 1/2 - loss 0.06166407 - time (sec): 0.08 - samples/sec: 6742.59 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,720 epoch 104 - iter 2/2 - loss 0.06257367 - time (sec): 0.18 - samples/sec: 6019.34 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,721 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,724 EPOCH 104 done: loss 0.0626 - lr: 0.000781\n", "2024-04-02 12:39:05,726 - 3 epochs without improvement\n", "2024-04-02 12:39:05,728 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,834 epoch 105 - iter 1/2 - loss 0.07319657 - time (sec): 0.10 - samples/sec: 5565.30 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,915 epoch 105 - iter 2/2 - loss 0.06395451 - time (sec): 0.19 - samples/sec: 5856.23 - lr: 0.000781 - momentum: 0.000000\n", "2024-04-02 12:39:05,922 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:05,924 EPOCH 105 done: loss 0.0640 - lr: 0.000781\n", "2024-04-02 12:39:05,926 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.000390625]\n", "2024-04-02 12:39:05,929 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,018 epoch 106 - iter 1/2 - loss 0.05404112 - time (sec): 0.09 - samples/sec: 6505.52 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,114 epoch 106 - iter 2/2 - loss 0.07654067 - time (sec): 0.18 - samples/sec: 5959.38 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,116 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,118 EPOCH 106 done: loss 0.0765 - lr: 0.000391\n", "2024-04-02 12:39:06,120 - 1 epochs without improvement\n", "2024-04-02 12:39:06,122 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,228 epoch 107 - iter 1/2 - loss 0.06587247 - time (sec): 0.10 - samples/sec: 5804.27 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,326 epoch 107 - iter 2/2 - loss 0.07110983 - time (sec): 0.20 - samples/sec: 5400.98 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,327 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,329 EPOCH 107 done: loss 0.0711 - lr: 0.000391\n", "2024-04-02 12:39:06,331 - 2 epochs without improvement\n", "2024-04-02 12:39:06,333 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,432 epoch 108 - iter 1/2 - loss 0.07758797 - time (sec): 0.10 - samples/sec: 6020.48 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,532 epoch 108 - iter 2/2 - loss 0.07718707 - time (sec): 0.20 - samples/sec: 5554.28 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,535 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,536 EPOCH 108 done: loss 0.0772 - lr: 0.000391\n", "2024-04-02 12:39:06,538 - 3 epochs without improvement\n", "2024-04-02 12:39:06,540 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,664 epoch 109 - iter 1/2 - loss 0.05915363 - time (sec): 0.12 - samples/sec: 4673.94 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,794 epoch 109 - iter 2/2 - loss 0.05913773 - time (sec): 0.25 - samples/sec: 4323.62 - lr: 0.000391 - momentum: 0.000000\n", "2024-04-02 12:39:06,796 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,798 EPOCH 109 done: loss 0.0591 - lr: 0.000391\n", "2024-04-02 12:39:06,800 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0001953125]\n", "2024-04-02 12:39:06,801 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:06,938 epoch 110 - iter 1/2 - loss 0.06734865 - time (sec): 0.13 - samples/sec: 4363.30 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,048 epoch 110 - iter 2/2 - loss 0.06403043 - time (sec): 0.24 - samples/sec: 4442.16 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,051 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,053 EPOCH 110 done: loss 0.0640 - lr: 0.000195\n", "2024-04-02 12:39:07,055 - 1 epochs without improvement\n", "2024-04-02 12:39:07,057 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,165 epoch 111 - iter 1/2 - loss 0.08403663 - time (sec): 0.11 - samples/sec: 5375.66 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,295 epoch 111 - iter 2/2 - loss 0.08189290 - time (sec): 0.24 - samples/sec: 4599.19 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,302 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,304 EPOCH 111 done: loss 0.0819 - lr: 0.000195\n", "2024-04-02 12:39:07,306 - 2 epochs without improvement\n", "2024-04-02 12:39:07,310 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,447 epoch 112 - iter 1/2 - loss 0.06873646 - time (sec): 0.13 - samples/sec: 4332.50 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,550 epoch 112 - iter 2/2 - loss 0.07077024 - time (sec): 0.24 - samples/sec: 4566.74 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,553 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,554 EPOCH 112 done: loss 0.0708 - lr: 0.000195\n", "2024-04-02 12:39:07,556 - 3 epochs without improvement\n", "2024-04-02 12:39:07,558 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,691 epoch 113 - iter 1/2 - loss 0.06367003 - time (sec): 0.13 - samples/sec: 4449.81 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,802 epoch 113 - iter 2/2 - loss 0.07219690 - time (sec): 0.24 - samples/sec: 4490.01 - lr: 0.000195 - momentum: 0.000000\n", "2024-04-02 12:39:07,806 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,809 EPOCH 113 done: loss 0.0722 - lr: 0.000195\n", "2024-04-02 12:39:07,811 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [9.765625e-05]\n", "2024-04-02 12:39:07,813 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,815 learning rate too small - quitting training!\n", "2024-04-02 12:39:07,817 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:07,819 Saving model ...\n", "2024-04-02 12:39:09,605 Done.\n", "2024-04-02 12:39:09,607 ----------------------------------------------------------------------------------------------------\n", "2024-04-02 12:39:09,611 Testing using last state of model ...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.71it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 12:39:09,914 \n", "Results:\n", "- F-score (micro) 0.9091\n", "- F-score (macro) 0.9\n", "- Accuracy 0.8333\n", "\n", "By class:\n", " precision recall f1-score support\n", "\n", " GCNUM 1.0000 1.0000 1.0000 3\n", " TRACK-ID 1.0000 0.6667 0.8000 3\n", "\n", " micro avg 1.0000 0.8333 0.9091 6\n", " macro avg 1.0000 0.8333 0.9000 6\n", "weighted avg 1.0000 0.8333 0.9000 6\n", "\n", "2024-04-02 12:39:09,916 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_score': 0.9090909090909091}" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [ "import torch\n", "torch.cuda.empty_cache()" ], "metadata": { "id": "Wp195IvjvS-d" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from flair.data import Sentence\n", "from flair.models import SequenceTagger\n", "# load the trained model\n", "model = SequenceTagger.load('/content/resources/taggers/ner-english/final-model.pt')\n", "# create example sentence\n", "sentence = Sentence('DOOMDAY WHITESTOWN INADS dipak manavr dipak manvar GC14024 INDIA FL55 BG2303140267275 GROUND OnTrac')\n", "# predict the tags\n", "model.predict(sentence)\n", "print(sentence.to_tagged_string())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tkBblzFUrFdT", "outputId": "93fa8370-cbae-48dc-bdaf-fd57f3dc5129" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2024-04-02 06:50:57,549 SequenceTagger predicts: Dictionary with 11 tags: O, S-GCNUM, B-GCNUM, E-GCNUM, I-GCNUM, S-TRACK-ID, B-TRACK-ID, E-TRACK-ID, I-TRACK-ID, , \n", "Sentence[13]: \"DOOMDAY WHITESTOWN INADS dipak manavr dipak manvar GC14024 INDIA FL55 BG2303140267275 GROUND OnTrac\" → [\"GC14024\"/GCNUM, \"BG2303140267275\"/TRACK-ID]\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "torch.save(model.state_dict(), '/content/drive/MyDrive/model.bin')" ], "metadata": { "id": "Xfs7BLwJtpNP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "corpus.train[0].to_tagged_string('ner')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "id": "zpy_Yennoh7n", "outputId": "ea4dd288-396c-4b50-d13f-d27a736db970" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'Sentence[10]: \"ROADGET LINTHA SMITH GC13124 MIAMI FL33155 BG2303140267275 GROUND OnTrac LINTA\" → [\"ROADGET\"/ORG, \"LINTHA SMITH\"/NAME, \"GC13124\"/GCNUMBER, \"MIAMI FL33155\"/LOCATION, \"BG2303140267275\"/BGNUMBER, \"LINTA\"/NAME]'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 43 } ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-9kVdxcjKe-P", "outputId": "4ace1e3b-0dc7-42fe-82c1-be8786709322" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "!pip freeze" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GhocZeE5sjl2", "outputId": "326e9624-12fb-45bc-81ed-1b8028b0e6fb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "absl-py==1.4.0\n", "accelerate==0.28.0\n", "aiohttp==3.9.3\n", "aiosignal==1.3.1\n", "alabaster==0.7.16\n", "albumentations==1.3.1\n", "altair==4.2.2\n", "annotated-types==0.6.0\n", "anyio==3.7.1\n", "appdirs==1.4.4\n", "argon2-cffi==23.1.0\n", "argon2-cffi-bindings==21.2.0\n", "array-record==0.5.0\n", "arviz==0.15.1\n", "astropy==5.3.4\n", "astunparse==1.6.3\n", "async-timeout==4.0.3\n", "atpublic==4.0\n", "attrs==23.2.0\n", "audioread==3.0.1\n", "autograd==1.6.2\n", "Babel==2.14.0\n", "backcall==0.2.0\n", "beautifulsoup4==4.12.3\n", "bidict==0.23.1\n", "bigframes==1.0.0\n", "bleach==6.1.0\n", "blinker==1.4\n", "blis==0.7.11\n", "blosc2==2.0.0\n", "bokeh==3.3.4\n", "boto3==1.34.72\n", "botocore==1.34.72\n", "bpemb==0.3.5\n", "bqplot==0.12.43\n", "branca==0.7.1\n", "build==1.1.1\n", "CacheControl==0.14.0\n", "cachetools==5.3.3\n", "catalogue==2.0.10\n", "certifi==2024.2.2\n", "cffi==1.16.0\n", "chardet==5.2.0\n", "charset-normalizer==3.3.2\n", "chex==0.1.86\n", "click==8.1.7\n", "click-plugins==1.1.1\n", "cligj==0.7.2\n", "cloudpathlib==0.16.0\n", "cloudpickle==2.2.1\n", "cmake==3.27.9\n", "cmdstanpy==1.2.1\n", "colorcet==3.1.0\n", "colorlover==0.3.0\n", "colour==0.1.5\n", "community==1.0.0b1\n", "confection==0.1.4\n", "conllu==4.5.3\n", "cons==0.4.6\n", "contextlib2==21.6.0\n", "contourpy==1.2.0\n", "cryptography==42.0.5\n", "cufflinks==0.17.3\n", "cupy-cuda12x==12.2.0\n", "cvxopt==1.3.2\n", "cvxpy==1.3.3\n", "cycler==0.12.1\n", "cymem==2.0.8\n", "Cython==3.0.9\n", "dask==2023.8.1\n", "datascience==0.17.6\n", "db-dtypes==1.2.0\n", "dbus-python==1.2.18\n", "debugpy==1.6.6\n", "decorator==4.4.2\n", "defusedxml==0.7.1\n", "Deprecated==1.2.14\n", "distributed==2023.8.1\n", "distro==1.7.0\n", "dlib==19.24.2\n", "dm-tree==0.1.8\n", "docutils==0.18.1\n", "dopamine-rl==4.0.6\n", "duckdb==0.9.2\n", "earthengine-api==0.1.395\n", "easydict==1.13\n", "ecos==2.0.13\n", "editdistance==0.6.2\n", "eerepr==0.0.4\n", "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889\n", "entrypoints==0.4\n", "et-xmlfile==1.1.0\n", "etils==1.7.0\n", "etuples==0.3.9\n", "exceptiongroup==1.2.0\n", "fastai==2.7.14\n", "fastcore==1.5.29\n", "fastdownload==0.0.7\n", "fastjsonschema==2.19.1\n", "fastprogress==1.0.3\n", "fastrlock==0.8.2\n", "filelock==3.13.3\n", "fiona==1.9.6\n", "firebase-admin==5.3.0\n", "flair==0.13.1\n", "Flask==2.2.5\n", "flatbuffers==24.3.7\n", "flax==0.8.2\n", "folium==0.14.0\n", "fonttools==4.50.0\n", "frozendict==2.4.0\n", "frozenlist==1.4.1\n", "fsspec==2023.6.0\n", "ftfy==6.2.0\n", "future==0.18.3\n", "gast==0.5.4\n", "gcsfs==2023.6.0\n", "GDAL==3.6.4\n", "gdown==4.7.3\n", "geemap==0.32.0\n", "gensim==4.3.2\n", "geocoder==1.38.1\n", "geographiclib==2.0\n", "geopandas==0.13.2\n", "geopy==2.3.0\n", "gin-config==0.5.0\n", "glob2==0.7\n", "google==2.0.3\n", "google-ai-generativelanguage==0.4.0\n", "google-api-core==2.11.1\n", "google-api-python-client==2.84.0\n", "google-auth==2.27.0\n", "google-auth-httplib2==0.1.1\n", "google-auth-oauthlib==1.2.0\n", "google-cloud-aiplatform==1.44.0\n", "google-cloud-bigquery==3.12.0\n", "google-cloud-bigquery-connection==1.12.1\n", "google-cloud-bigquery-storage==2.24.0\n", "google-cloud-core==2.3.3\n", "google-cloud-datastore==2.15.2\n", "google-cloud-firestore==2.11.1\n", "google-cloud-functions==1.13.3\n", "google-cloud-iam==2.14.3\n", "google-cloud-language==2.13.3\n", "google-cloud-resource-manager==1.12.3\n", "google-cloud-storage==2.8.0\n", "google-cloud-translate==3.11.3\n", "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=0b702febc660d4a9a3e78e6568740624daa9ccacc4b00e81f209868b7ef4c290\n", "google-crc32c==1.5.0\n", "google-generativeai==0.3.2\n", "google-pasta==0.2.0\n", "google-resumable-media==2.7.0\n", "googleapis-common-protos==1.63.0\n", "googledrivedownloader==0.4\n", "graphviz==0.20.3\n", "greenlet==3.0.3\n", "grpc-google-iam-v1==0.13.0\n", "grpcio==1.62.1\n", "grpcio-status==1.48.2\n", "gspread==3.4.2\n", "gspread-dataframe==3.3.1\n", "gym==0.25.2\n", "gym-notices==0.0.8\n", "h5netcdf==1.3.0\n", "h5py==3.9.0\n", "holidays==0.45\n", "holoviews==1.17.1\n", "html5lib==1.1\n", "httpimport==1.3.1\n", "httplib2==0.22.0\n", "huggingface-hub==0.20.3\n", "humanize==4.7.0\n", "hyperopt==0.2.7\n", "ibis-framework==8.0.0\n", "idna==3.6\n", "imageio==2.31.6\n", "imageio-ffmpeg==0.4.9\n", "imagesize==1.4.1\n", "imbalanced-learn==0.10.1\n", "imgaug==0.4.0\n", "importlib_metadata==7.1.0\n", "importlib_resources==6.4.0\n", "imutils==0.5.4\n", "inflect==7.0.0\n", "iniconfig==2.0.0\n", "intel-openmp==2023.2.4\n", "ipyevents==2.0.2\n", "ipyfilechooser==0.6.0\n", "ipykernel==5.5.6\n", "ipyleaflet==0.18.2\n", "ipython==7.34.0\n", "ipython-genutils==0.2.0\n", "ipython-sql==0.5.0\n", "ipytree==0.2.2\n", "ipywidgets==7.7.1\n", "itsdangerous==2.1.2\n", "Janome==0.5.0\n", "jax==0.4.23\n", "jaxlib @ https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.23+cuda12.cudnn89-cp310-cp310-manylinux2014_x86_64.whl#sha256=8e42000672599e7ec0ea7f551acfcc95dcdd0e22b05a1d1f12f97b56a9fce4a8\n", "jeepney==0.7.1\n", "jieba==0.42.1\n", "Jinja2==3.1.3\n", "jmespath==1.0.1\n", "joblib==1.3.2\n", "jsonpickle==3.0.3\n", "jsonschema==4.19.2\n", "jsonschema-specifications==2023.12.1\n", "jupyter-client==6.1.12\n", "jupyter-console==6.1.0\n", "jupyter-server==1.24.0\n", "jupyter_core==5.7.2\n", "jupyterlab_pygments==0.3.0\n", "jupyterlab_widgets==3.0.10\n", "kaggle==1.5.16\n", "kagglehub==0.2.1\n", "keras==2.15.0\n", "keyring==23.5.0\n", "kiwisolver==1.4.5\n", "langcodes==3.3.0\n", "langdetect==1.0.9\n", "launchpadlib==1.10.16\n", "lazr.restfulclient==0.14.4\n", "lazr.uri==1.0.6\n", "lazy_loader==0.3\n", "libclang==18.1.1\n", "librosa==0.10.1\n", "lightgbm==4.1.0\n", "linkify-it-py==2.0.3\n", "llvmlite==0.41.1\n", "locket==1.0.0\n", "logical-unification==0.4.6\n", "lxml==4.9.4\n", "malloy==2023.1067\n", "Markdown==3.6\n", "markdown-it-py==3.0.0\n", "MarkupSafe==2.1.5\n", "matplotlib==3.7.1\n", "matplotlib-inline==0.1.6\n", "matplotlib-venn==0.11.10\n", "mdit-py-plugins==0.4.0\n", "mdurl==0.1.2\n", "miniKanren==1.0.3\n", "missingno==0.5.2\n", "mistune==0.8.4\n", "mizani==0.9.3\n", "mkl==2023.2.0\n", "ml-dtypes==0.2.0\n", "mlxtend==0.22.0\n", "more-itertools==10.1.0\n", "moviepy==1.0.3\n", "mpld3==0.5.10\n", "mpmath==1.3.0\n", "msgpack==1.0.8\n", "multidict==6.0.5\n", "multipledispatch==1.0.0\n", "multitasking==0.0.11\n", "murmurhash==1.0.10\n", "music21==9.1.0\n", "natsort==8.4.0\n", "nbclassic==1.0.0\n", "nbclient==0.10.0\n", "nbconvert==6.5.4\n", "nbformat==5.10.3\n", "nest-asyncio==1.6.0\n", "networkx==3.2.1\n", "nibabel==4.0.2\n", "nltk==3.8.1\n", "notebook==6.5.5\n", "notebook_shim==0.2.4\n", "numba==0.58.1\n", "numexpr==2.9.0\n", "numpy==1.25.2\n", "nvidia-cublas-cu12==12.1.3.1\n", "nvidia-cuda-cupti-cu12==12.1.105\n", "nvidia-cuda-nvrtc-cu12==12.1.105\n", "nvidia-cuda-runtime-cu12==12.1.105\n", "nvidia-cudnn-cu12==8.9.2.26\n", "nvidia-cufft-cu12==11.0.2.54\n", "nvidia-curand-cu12==10.3.2.106\n", "nvidia-cusolver-cu12==11.4.5.107\n", "nvidia-cusparse-cu12==12.1.0.106\n", "nvidia-nccl-cu12==2.19.3\n", "nvidia-nvjitlink-cu12==12.4.99\n", "nvidia-nvtx-cu12==12.1.105\n", "oauth2client==4.1.3\n", "oauthlib==3.2.2\n", "opencv-contrib-python==4.8.0.76\n", "opencv-python==4.8.0.76\n", "opencv-python-headless==4.9.0.80\n", "openpyxl==3.1.2\n", "opt-einsum==3.3.0\n", "optax==0.2.1\n", "orbax-checkpoint==0.4.4\n", "osqp==0.6.2.post8\n", "packaging==24.0\n", "pandas==1.5.3\n", "pandas-datareader==0.10.0\n", "pandas-gbq==0.19.2\n", "pandas-stubs==1.5.3.230304\n", "pandocfilters==1.5.1\n", "panel==1.3.8\n", "param==2.1.0\n", "parso==0.8.3\n", "parsy==2.1\n", "partd==1.4.1\n", "pathlib==1.0.1\n", "patsy==0.5.6\n", "peewee==3.17.1\n", "pexpect==4.9.0\n", "pickleshare==0.7.5\n", "Pillow==9.4.0\n", "pip-tools==6.13.0\n", "platformdirs==4.2.0\n", "plotly==5.15.0\n", "plotnine==0.12.4\n", "pluggy==1.4.0\n", "polars==0.20.2\n", "pooch==1.8.1\n", "portpicker==1.5.2\n", "pptree==3.1\n", "prefetch-generator==1.0.3\n", "preshed==3.0.9\n", "prettytable==3.10.0\n", "proglog==0.1.10\n", "progressbar2==4.2.0\n", "prometheus_client==0.20.0\n", "promise==2.3\n", "prompt-toolkit==3.0.43\n", "prophet==1.1.5\n", "proto-plus==1.23.0\n", "protobuf==3.20.3\n", "psutil==5.9.5\n", "psycopg2==2.9.9\n", "ptyprocess==0.7.0\n", "py-cpuinfo==9.0.0\n", "py4j==0.10.9.7\n", "pyarrow==14.0.2\n", "pyarrow-hotfix==0.6\n", "pyasn1==0.5.1\n", "pyasn1-modules==0.3.0\n", "pycocotools==2.0.7\n", "pycparser==2.21\n", "pydantic==2.6.4\n", "pydantic_core==2.16.3\n", "pydata-google-auth==1.8.2\n", "pydot==1.4.2\n", "pydot-ng==2.0.0\n", "pydotplus==2.0.2\n", "PyDrive==1.3.1\n", "PyDrive2==1.6.3\n", "pyerfa==2.0.1.1\n", "pygame==2.5.2\n", "Pygments==2.16.1\n", "PyGObject==3.42.1\n", "PyJWT==2.3.0\n", "pymc==5.10.4\n", "pymystem3==0.2.0\n", "PyOpenGL==3.1.7\n", "pyOpenSSL==24.1.0\n", "pyparsing==3.1.2\n", "pyperclip==1.8.2\n", "pyproj==3.6.1\n", "pyproject_hooks==1.0.0\n", "pyshp==2.3.1\n", "PySocks==1.7.1\n", "pytensor==2.18.6\n", "pytest==7.4.4\n", "python-apt @ file:///backend-container/containers/python_apt-0.0.0-cp310-cp310-linux_x86_64.whl#sha256=b209c7165d6061963abe611492f8c91c3bcef4b7a6600f966bab58900c63fefa\n", "python-box==7.1.1\n", "python-dateutil==2.8.2\n", "python-louvain==0.16\n", "python-slugify==8.0.4\n", "python-utils==3.8.2\n", "pytorch_revgrad==0.2.0\n", "pytz==2023.4\n", "pyviz_comms==3.0.2\n", "PyWavelets==1.5.0\n", "PyYAML==6.0.1\n", "pyzmq==23.2.1\n", "qdldl==0.1.7.post0\n", "qudida==0.0.4\n", "ratelim==0.1.6\n", "referencing==0.34.0\n", "regex==2023.12.25\n", "requests==2.31.0\n", "requests-oauthlib==1.4.0\n", "requirements-parser==0.5.0\n", "rich==13.7.1\n", "rpds-py==0.18.0\n", "rpy2==3.4.2\n", "rsa==4.9\n", "s3transfer==0.10.1\n", "safetensors==0.4.2\n", "scikit-image==0.19.3\n", "scikit-learn==1.2.2\n", "scipy==1.11.4\n", "scooby==0.9.2\n", "scs==3.2.4.post1\n", "seaborn==0.13.1\n", "SecretStorage==3.3.1\n", "segtok==1.5.11\n", "semver==3.0.2\n", "Send2Trash==1.8.2\n", "sentencepiece==0.1.99\n", "shapely==2.0.3\n", "six==1.16.0\n", "sklearn-pandas==2.2.0\n", "smart-open==6.4.0\n", "sniffio==1.3.1\n", "snowballstemmer==2.2.0\n", "sortedcontainers==2.4.0\n", "soundfile==0.12.1\n", "soupsieve==2.5\n", "soxr==0.3.7\n", "spacy==3.7.4\n", "spacy-legacy==3.0.12\n", "spacy-loggers==1.0.5\n", "Sphinx==5.0.2\n", "sphinxcontrib-applehelp==1.0.8\n", "sphinxcontrib-devhelp==1.0.6\n", "sphinxcontrib-htmlhelp==2.0.5\n", "sphinxcontrib-jsmath==1.0.1\n", "sphinxcontrib-qthelp==1.0.7\n", "sphinxcontrib-serializinghtml==1.1.10\n", "SQLAlchemy==2.0.29\n", "sqlglot==20.11.0\n", "sqlitedict==2.1.0\n", "sqlparse==0.4.4\n", "srsly==2.4.8\n", "stanio==0.3.0\n", "statsmodels==0.14.1\n", "sympy==1.12\n", "tables==3.8.0\n", "tabulate==0.9.0\n", "tbb==2021.11.0\n", "tblib==3.0.0\n", "tenacity==8.2.3\n", "tensorboard==2.15.2\n", "tensorboard-data-server==0.7.2\n", "tensorflow==2.15.0\n", "tensorflow-datasets==4.9.4\n", "tensorflow-estimator==2.15.0\n", "tensorflow-gcs-config==2.15.0\n", "tensorflow-hub==0.16.1\n", "tensorflow-io-gcs-filesystem==0.36.0\n", "tensorflow-metadata==1.14.0\n", "tensorflow-probability==0.23.0\n", "tensorstore==0.1.45\n", "termcolor==2.4.0\n", "terminado==0.18.1\n", "text-unidecode==1.3\n", "textblob==0.17.1\n", "tf-slim==1.1.0\n", "tf_keras==2.15.1\n", "thinc==8.2.3\n", "threadpoolctl==3.4.0\n", "tifffile==2024.2.12\n", "tinycss2==1.2.1\n", "tokenizers==0.15.2\n", "toml==0.10.2\n", "tomli==2.0.1\n", "toolz==0.12.1\n", "torch @ https://download.pytorch.org/whl/cu121/torch-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=1adf430f01ff649c848ac021785e18007b0714fdde68e4e65bd0c640bf3fb8e1\n", "torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=23f6236429e2bf676b820e8e7221a1d58aaf908bff2ba2665aa852df71a97961\n", "torchdata==0.7.1\n", "torchsummary==1.5.1\n", "torchtext==0.17.1\n", "torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.17.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=27af47915f6e762c1d44e58e8088d22ac97445668f9f793524032b2baf4f34bd\n", "tornado==6.3.3\n", "tqdm==4.66.2\n", "traitlets==5.7.1\n", "traittypes==0.2.1\n", "transformer-smaller-training-vocab==0.3.3\n", "transformers==4.38.2\n", "triton==2.2.0\n", "tweepy==4.14.0\n", "typer==0.9.4\n", "types-pytz==2024.1.0.20240203\n", "types-setuptools==69.2.0.20240317\n", "typing_extensions==4.10.0\n", "tzlocal==5.2\n", "uc-micro-py==1.0.3\n", "uritemplate==4.1.1\n", "urllib3==1.26.18\n", "vega-datasets==0.9.0\n", "wadllib==1.3.6\n", "wasabi==1.1.2\n", "wcwidth==0.2.13\n", "weasel==0.3.4\n", "webcolors==1.13\n", "webencodings==0.5.1\n", "websocket-client==1.7.0\n", "Werkzeug==3.0.1\n", "widgetsnbextension==3.6.6\n", "Wikipedia-API==0.6.0\n", "wordcloud==1.9.3\n", "wrapt==1.14.1\n", "xarray==2023.7.0\n", "xarray-einstats==0.7.0\n", "xgboost==2.0.3\n", "xlrd==2.0.1\n", "xyzservices==2023.10.1\n", "yarl==1.9.4\n", "yellowbrick==1.5\n", "yfinance==0.2.37\n", "zict==3.0.0\n", "zipp==3.18.1\n" ] } ] }, { "cell_type": "code", "source": [ "!pip freeze" ], "metadata": { "id": "aHb6ICTuvUQW", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d843f688-9034-416c-fbd1-98a8545740df" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "absl-py==1.4.0\n", "accelerate==0.28.0\n", "aiohttp==3.9.3\n", "aiosignal==1.3.1\n", "alabaster==0.7.16\n", "albumentations==1.3.1\n", "altair==4.2.2\n", "annotated-types==0.6.0\n", "anyio==3.7.1\n", "appdirs==1.4.4\n", "argon2-cffi==23.1.0\n", "argon2-cffi-bindings==21.2.0\n", "array-record==0.5.0\n", "arviz==0.15.1\n", "astropy==5.3.4\n", "astunparse==1.6.3\n", "async-timeout==4.0.3\n", "atpublic==4.0\n", "attrs==23.2.0\n", "audioread==3.0.1\n", "autograd==1.6.2\n", "Babel==2.14.0\n", "backcall==0.2.0\n", "beautifulsoup4==4.12.3\n", "bidict==0.23.1\n", "bigframes==1.0.0\n", "bleach==6.1.0\n", "blinker==1.4\n", "blis==0.7.11\n", "blosc2==2.0.0\n", "bokeh==3.3.4\n", "boto3==1.34.75\n", "botocore==1.34.75\n", "bpemb==0.3.5\n", "bqplot==0.12.43\n", "branca==0.7.1\n", "build==1.2.1\n", "CacheControl==0.14.0\n", "cachetools==5.3.3\n", "catalogue==2.0.10\n", "certifi==2024.2.2\n", "cffi==1.16.0\n", "chardet==5.2.0\n", "charset-normalizer==3.3.2\n", "chex==0.1.86\n", "click==8.1.7\n", "click-plugins==1.1.1\n", "cligj==0.7.2\n", "cloudpathlib==0.16.0\n", "cloudpickle==2.2.1\n", "cmake==3.27.9\n", "cmdstanpy==1.2.2\n", "colorcet==3.1.0\n", "colorlover==0.3.0\n", "colour==0.1.5\n", "community==1.0.0b1\n", "confection==0.1.4\n", "conllu==4.5.3\n", "cons==0.4.6\n", "contextlib2==21.6.0\n", "contourpy==1.2.0\n", "cryptography==42.0.5\n", "cufflinks==0.17.3\n", "cupy-cuda12x==12.2.0\n", "cvxopt==1.3.2\n", "cvxpy==1.3.3\n", "cycler==0.12.1\n", "cymem==2.0.8\n", "Cython==3.0.9\n", "dask==2023.8.1\n", "datascience==0.17.6\n", "datasets==2.18.0\n", "db-dtypes==1.2.0\n", "dbus-python==1.2.18\n", "debugpy==1.6.6\n", "decorator==4.4.2\n", "defusedxml==0.7.1\n", "Deprecated==1.2.14\n", "dill==0.3.8\n", "distributed==2023.8.1\n", "distro==1.7.0\n", "dlib==19.24.2\n", "dm-tree==0.1.8\n", "docstring_parser==0.16\n", "docutils==0.18.1\n", "dopamine-rl==4.0.6\n", "duckdb==0.9.2\n", "earthengine-api==0.1.395\n", "easydict==1.13\n", "ecos==2.0.13\n", "editdistance==0.6.2\n", "eerepr==0.0.4\n", "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889\n", "entrypoints==0.4\n", "et-xmlfile==1.1.0\n", "etils==1.7.0\n", "etuples==0.3.9\n", "evaluate==0.4.1\n", "exceptiongroup==1.2.0\n", "fastai==2.7.14\n", "fastcore==1.5.29\n", "fastdownload==0.0.7\n", "fastjsonschema==2.19.1\n", "fastprogress==1.0.3\n", "fastrlock==0.8.2\n", "filelock==3.13.3\n", "fiona==1.9.6\n", "firebase-admin==5.3.0\n", "flair==0.13.1\n", "Flask==2.2.5\n", "flatbuffers==24.3.25\n", "flax==0.8.2\n", "folium==0.14.0\n", "fonttools==4.50.0\n", "frozendict==2.4.0\n", "frozenlist==1.4.1\n", "fsspec==2023.6.0\n", "ftfy==6.2.0\n", "future==0.18.3\n", "gast==0.5.4\n", "gcsfs==2023.6.0\n", "GDAL==3.6.4\n", "gdown==4.7.3\n", "geemap==0.32.0\n", "gensim==4.3.2\n", "geocoder==1.38.1\n", "geographiclib==2.0\n", "geopandas==0.13.2\n", "geopy==2.3.0\n", "gin-config==0.5.0\n", "glob2==0.7\n", "google==2.0.3\n", "google-ai-generativelanguage==0.4.0\n", "google-api-core==2.11.1\n", "google-api-python-client==2.84.0\n", "google-auth==2.27.0\n", "google-auth-httplib2==0.1.1\n", "google-auth-oauthlib==1.2.0\n", "google-cloud-aiplatform==1.45.0\n", "google-cloud-bigquery==3.12.0\n", "google-cloud-bigquery-connection==1.12.1\n", "google-cloud-bigquery-storage==2.24.0\n", "google-cloud-core==2.3.3\n", "google-cloud-datastore==2.15.2\n", "google-cloud-firestore==2.11.1\n", "google-cloud-functions==1.13.3\n", "google-cloud-iam==2.14.3\n", "google-cloud-language==2.13.3\n", "google-cloud-resource-manager==1.12.3\n", "google-cloud-storage==2.8.0\n", "google-cloud-translate==3.11.3\n", "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=b74977b504f63978fe0c9f7c4a1c6208e068811827433cbb8bd6159529477277\n", "google-crc32c==1.5.0\n", "google-generativeai==0.3.2\n", "google-pasta==0.2.0\n", "google-resumable-media==2.7.0\n", "googleapis-common-protos==1.63.0\n", "googledrivedownloader==0.4\n", "graphviz==0.20.3\n", "greenlet==3.0.3\n", "grpc-google-iam-v1==0.13.0\n", "grpcio==1.62.1\n", "grpcio-status==1.48.2\n", "gspread==3.4.2\n", "gspread-dataframe==3.3.1\n", "gym==0.25.2\n", "gym-notices==0.0.8\n", "h5netcdf==1.3.0\n", "h5py==3.9.0\n", "holidays==0.45\n", "holoviews==1.17.1\n", "html5lib==1.1\n", "httpimport==1.3.1\n", "httplib2==0.22.0\n", "huggingface-hub==0.20.3\n", "humanize==4.7.0\n", "hyperopt==0.2.7\n", "ibis-framework==8.0.0\n", "idna==3.6\n", "imageio==2.31.6\n", "imageio-ffmpeg==0.4.9\n", "imagesize==1.4.1\n", "imbalanced-learn==0.10.1\n", "imgaug==0.4.0\n", "importlib_metadata==7.1.0\n", "importlib_resources==6.4.0\n", "imutils==0.5.4\n", "inflect==7.0.0\n", "iniconfig==2.0.0\n", "intel-openmp==2023.2.4\n", "ipyevents==2.0.2\n", "ipyfilechooser==0.6.0\n", "ipykernel==5.5.6\n", "ipyleaflet==0.18.2\n", "ipython==7.34.0\n", "ipython-genutils==0.2.0\n", "ipython-sql==0.5.0\n", "ipytree==0.2.2\n", "ipywidgets==7.7.1\n", "itsdangerous==2.1.2\n", "Janome==0.5.0\n", "jax==0.4.23\n", "jaxlib @ https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.23+cuda12.cudnn89-cp310-cp310-manylinux2014_x86_64.whl#sha256=8e42000672599e7ec0ea7f551acfcc95dcdd0e22b05a1d1f12f97b56a9fce4a8\n", "jeepney==0.7.1\n", "jieba==0.42.1\n", "Jinja2==3.1.3\n", "jmespath==1.0.1\n", "joblib==1.3.2\n", "jsonpickle==3.0.3\n", "jsonschema==4.19.2\n", "jsonschema-specifications==2023.12.1\n", "jupyter-client==6.1.12\n", "jupyter-console==6.1.0\n", "jupyter-server==1.24.0\n", "jupyter_core==5.7.2\n", "jupyterlab_pygments==0.3.0\n", "jupyterlab_widgets==3.0.10\n", "kaggle==1.5.16\n", "kagglehub==0.2.2\n", "keras==2.15.0\n", "keyring==23.5.0\n", "kiwisolver==1.4.5\n", "langcodes==3.3.0\n", "langdetect==1.0.9\n", "launchpadlib==1.10.16\n", "lazr.restfulclient==0.14.4\n", "lazr.uri==1.0.6\n", "lazy_loader==0.3\n", "libclang==18.1.1\n", "librosa==0.10.1\n", "lightgbm==4.1.0\n", "linkify-it-py==2.0.3\n", "llvmlite==0.41.1\n", "locket==1.0.0\n", "logical-unification==0.4.6\n", "lxml==4.9.4\n", "malloy==2023.1067\n", "Markdown==3.6\n", "markdown-it-py==3.0.0\n", "MarkupSafe==2.1.5\n", "matplotlib==3.7.1\n", "matplotlib-inline==0.1.6\n", "matplotlib-venn==0.11.10\n", "mdit-py-plugins==0.4.0\n", "mdurl==0.1.2\n", "miniKanren==1.0.3\n", "missingno==0.5.2\n", "mistune==0.8.4\n", "mizani==0.9.3\n", "mkl==2023.2.0\n", "ml-dtypes==0.2.0\n", "mlxtend==0.22.0\n", "more-itertools==10.1.0\n", "moviepy==1.0.3\n", "mpld3==0.5.10\n", "mpmath==1.3.0\n", "msgpack==1.0.8\n", "multidict==6.0.5\n", "multipledispatch==1.0.0\n", "multiprocess==0.70.16\n", "multitasking==0.0.11\n", "murmurhash==1.0.10\n", "music21==9.1.0\n", "natsort==8.4.0\n", "nbclassic==1.0.0\n", "nbclient==0.10.0\n", "nbconvert==6.5.4\n", "nbformat==5.10.3\n", "nest-asyncio==1.6.0\n", "networkx==3.2.1\n", "nibabel==4.0.2\n", "nltk==3.8.1\n", "notebook==6.5.5\n", "notebook_shim==0.2.4\n", "numba==0.58.1\n", "numexpr==2.9.0\n", "numpy==1.25.2\n", "nvidia-cublas-cu12==12.1.3.1\n", "nvidia-cuda-cupti-cu12==12.1.105\n", "nvidia-cuda-nvrtc-cu12==12.1.105\n", "nvidia-cuda-runtime-cu12==12.1.105\n", "nvidia-cudnn-cu12==8.9.2.26\n", "nvidia-cufft-cu12==11.0.2.54\n", "nvidia-curand-cu12==10.3.2.106\n", "nvidia-cusolver-cu12==11.4.5.107\n", "nvidia-cusparse-cu12==12.1.0.106\n", "nvidia-nccl-cu12==2.19.3\n", "nvidia-nvjitlink-cu12==12.4.99\n", "nvidia-nvtx-cu12==12.1.105\n", "oauth2client==4.1.3\n", "oauthlib==3.2.2\n", "opencv-contrib-python==4.8.0.76\n", "opencv-python==4.8.0.76\n", "opencv-python-headless==4.9.0.80\n", "openpyxl==3.1.2\n", "opt-einsum==3.3.0\n", "optax==0.2.2\n", "orbax-checkpoint==0.4.4\n", "osqp==0.6.2.post8\n", "packaging==24.0\n", "pandas==1.5.3\n", "pandas-datareader==0.10.0\n", "pandas-gbq==0.19.2\n", "pandas-stubs==1.5.3.230304\n", "pandocfilters==1.5.1\n", "panel==1.3.8\n", "param==2.1.0\n", "parso==0.8.3\n", "parsy==2.1\n", "partd==1.4.1\n", "pathlib==1.0.1\n", "patsy==0.5.6\n", "peewee==3.17.1\n", "pexpect==4.9.0\n", "pickleshare==0.7.5\n", "Pillow==9.4.0\n", "pip-tools==6.13.0\n", "platformdirs==4.2.0\n", "plotly==5.15.0\n", "plotnine==0.12.4\n", "pluggy==1.4.0\n", "polars==0.20.2\n", "pooch==1.8.1\n", "portpicker==1.5.2\n", "pptree==3.1\n", "prefetch-generator==1.0.3\n", "preshed==3.0.9\n", "prettytable==3.10.0\n", "proglog==0.1.10\n", "progressbar2==4.2.0\n", "prometheus_client==0.20.0\n", "promise==2.3\n", "prompt-toolkit==3.0.43\n", "prophet==1.1.5\n", "proto-plus==1.23.0\n", "protobuf==3.20.3\n", "psutil==5.9.5\n", "psycopg2==2.9.9\n", "ptyprocess==0.7.0\n", "py-cpuinfo==9.0.0\n", "py4j==0.10.9.7\n", "pyarrow==14.0.2\n", "pyarrow-hotfix==0.6\n", "pyasn1==0.6.0\n", "pyasn1_modules==0.4.0\n", "pycocotools==2.0.7\n", "pycparser==2.21\n", "pydantic==2.6.4\n", "pydantic_core==2.16.3\n", "pydata-google-auth==1.8.2\n", "pydot==1.4.2\n", "pydot-ng==2.0.0\n", "pydotplus==2.0.2\n", "PyDrive==1.3.1\n", "PyDrive2==1.6.3\n", "pyerfa==2.0.1.1\n", "pygame==2.5.2\n", "Pygments==2.16.1\n", "PyGObject==3.42.1\n", "PyJWT==2.3.0\n", "pymc==5.10.4\n", "pymystem3==0.2.0\n", "PyOpenGL==3.1.7\n", "pyOpenSSL==24.1.0\n", "pyparsing==3.1.2\n", "pyperclip==1.8.2\n", "pyproj==3.6.1\n", "pyproject_hooks==1.0.0\n", "pyshp==2.3.1\n", "PySocks==1.7.1\n", "pytensor==2.18.6\n", "pytest==7.4.4\n", "python-apt @ file:///backend-container/containers/python_apt-0.0.0-cp310-cp310-linux_x86_64.whl#sha256=b209c7165d6061963abe611492f8c91c3bcef4b7a6600f966bab58900c63fefa\n", "python-box==7.1.1\n", "python-dateutil==2.8.2\n", "python-louvain==0.16\n", "python-slugify==8.0.4\n", "python-utils==3.8.2\n", "pytorch_revgrad==0.2.0\n", "pytz==2023.4\n", "pyviz_comms==3.0.2\n", "PyWavelets==1.5.0\n", "PyYAML==6.0.1\n", "pyzmq==23.2.1\n", "qdldl==0.1.7.post0\n", "qudida==0.0.4\n", "ratelim==0.1.6\n", "referencing==0.34.0\n", "regex==2023.12.25\n", "requests==2.31.0\n", "requests-oauthlib==1.4.1\n", "requirements-parser==0.7.0\n", "responses==0.18.0\n", "rich==13.7.1\n", "rpds-py==0.18.0\n", "rpy2==3.4.2\n", "rsa==4.9\n", "s3transfer==0.10.1\n", "safetensors==0.4.2\n", "scikit-image==0.19.3\n", "scikit-learn==1.2.2\n", "scipy==1.11.4\n", "scooby==0.9.2\n", "scs==3.2.4.post1\n", "seaborn==0.13.1\n", "SecretStorage==3.3.1\n", "segtok==1.5.11\n", "semver==3.0.2\n", "Send2Trash==1.8.2\n", "sentencepiece==0.1.99\n", "seqeval==1.2.2\n", "shapely==2.0.3\n", "six==1.16.0\n", "sklearn-pandas==2.2.0\n", "smart-open==6.4.0\n", "sniffio==1.3.1\n", "snowballstemmer==2.2.0\n", "sortedcontainers==2.4.0\n", "soundfile==0.12.1\n", "soupsieve==2.5\n", "soxr==0.3.7\n", "spacy==3.7.4\n", "spacy-legacy==3.0.12\n", "spacy-loggers==1.0.5\n", "Sphinx==5.0.2\n", "sphinxcontrib-applehelp==1.0.8\n", "sphinxcontrib-devhelp==1.0.6\n", "sphinxcontrib-htmlhelp==2.0.5\n", "sphinxcontrib-jsmath==1.0.1\n", "sphinxcontrib-qthelp==1.0.7\n", "sphinxcontrib-serializinghtml==1.1.10\n", "SQLAlchemy==2.0.29\n", "sqlglot==20.11.0\n", "sqlitedict==2.1.0\n", "sqlparse==0.4.4\n", "srsly==2.4.8\n", "stanio==0.5.0\n", "statsmodels==0.14.1\n", "sympy==1.12\n", "tables==3.8.0\n", "tabulate==0.9.0\n", "tbb==2021.12.0\n", "tblib==3.0.0\n", "tenacity==8.2.3\n", "tensorboard==2.15.2\n", "tensorboard-data-server==0.7.2\n", "tensorflow==2.15.0\n", "tensorflow-datasets==4.9.4\n", "tensorflow-estimator==2.15.0\n", "tensorflow-gcs-config==2.15.0\n", "tensorflow-hub==0.16.1\n", "tensorflow-io-gcs-filesystem==0.36.0\n", "tensorflow-metadata==1.14.0\n", "tensorflow-probability==0.23.0\n", "tensorstore==0.1.45\n", "termcolor==2.4.0\n", "terminado==0.18.1\n", "text-unidecode==1.3\n", "textblob==0.17.1\n", "tf-slim==1.1.0\n", "tf_keras==2.15.1\n", "thinc==8.2.3\n", "threadpoolctl==3.4.0\n", "tifffile==2024.2.12\n", "tinycss2==1.2.1\n", "tokenizers==0.15.2\n", "toml==0.10.2\n", "tomli==2.0.1\n", "toolz==0.12.1\n", "torch @ https://download.pytorch.org/whl/cu121/torch-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=1adf430f01ff649c848ac021785e18007b0714fdde68e4e65bd0c640bf3fb8e1\n", "torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=23f6236429e2bf676b820e8e7221a1d58aaf908bff2ba2665aa852df71a97961\n", "torchdata==0.7.1\n", "torchsummary==1.5.1\n", "torchtext==0.17.1\n", "torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.17.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=27af47915f6e762c1d44e58e8088d22ac97445668f9f793524032b2baf4f34bd\n", "tornado==6.3.3\n", "tqdm==4.66.2\n", "traitlets==5.7.1\n", "traittypes==0.2.1\n", "transformer-smaller-training-vocab==0.3.3\n", "transformers==4.39.2\n", "triton==2.2.0\n", "tweepy==4.14.0\n", "typer==0.9.4\n", "types-pytz==2024.1.0.20240203\n", "types-setuptools==69.2.0.20240317\n", "typing_extensions==4.10.0\n", "tzlocal==5.2\n", "uc-micro-py==1.0.3\n", "uritemplate==4.1.1\n", "urllib3==1.26.18\n", "vega-datasets==0.9.0\n", "wadllib==1.3.6\n", "wasabi==1.1.2\n", "wcwidth==0.2.13\n", "weasel==0.3.4\n", "webcolors==1.13\n", "webencodings==0.5.1\n", "websocket-client==1.7.0\n", "Werkzeug==3.0.1\n", "widgetsnbextension==3.6.6\n", "Wikipedia-API==0.6.0\n", "wordcloud==1.9.3\n", "wrapt==1.14.1\n", "xarray==2023.7.0\n", "xarray-einstats==0.7.0\n", "xgboost==2.0.3\n", "xlrd==2.0.1\n", "xxhash==3.4.1\n", "xyzservices==2023.10.1\n", "yarl==1.9.4\n", "yellowbrick==1.5\n", "yfinance==0.2.37\n", "zict==3.0.0\n", "zipp==3.18.1\n" ] } ] }, { "cell_type": "code", "source": [ "!python --version" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eYssGgM-R3Rn", "outputId": "e9d25a2e-9dba-46cd-ec3e-1bfe83745d08" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Python 3.10.12\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install pandas_profiling" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N27fEKgrPtH8", "outputId": "dcca85e7-fbaf-4de4-c594-fcae932e242c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting pandas_profiling\n", " Downloading pandas_profiling-3.6.6-py2.py3-none-any.whl (324 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m324.4/324.4 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting ydata-profiling (from pandas_profiling)\n", " Downloading ydata_profiling-4.7.0-py2.py3-none-any.whl (357 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m357.9/357.9 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 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wheels for collected packages: htmlmin\n", " Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27080 sha256=51cdf362db3a9c45d6f23d79c1a47acb04f8eb3c869b8cb9dc6bb4efa92eae5e\n", " Stored in directory: /root/.cache/pip/wheels/dd/91/29/a79cecb328d01739e64017b6fb9a1ab9d8cb1853098ec5966d\n", "Successfully built htmlmin\n", "Installing collected packages: htmlmin, typeguard, multimethod, dacite, imagehash, visions, seaborn, phik, ydata-profiling, pandas_profiling\n", " Attempting uninstall: seaborn\n", " Found existing installation: seaborn 0.13.1\n", " Uninstalling seaborn-0.13.1:\n", " Successfully uninstalled seaborn-0.13.1\n", "Successfully installed dacite-1.8.1 htmlmin-0.1.12 imagehash-4.3.1 multimethod-1.11.2 pandas_profiling-3.6.6 phik-0.12.4 seaborn-0.12.2 typeguard-4.2.1 visions-0.7.6 ydata-profiling-4.7.0\n" ] } ] }, { "cell_type": "code", "source": [ "# prompt: pandas profiler\n", "\n", "import pandas as pd\n", "# Load your dataset into a pandas DataFrame\n", "df = pd.read_csv('details.csv')\n", "\n", "\n" ], "metadata": { "id": "sT_uwnhoR4Te" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "import pandas as pd" ], "metadata": { "id": "tsZLsNdhQnY7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "pd.DataFrame(\n", " {'s1':[1,2,3,4],'s2':[3,4,5,6]}\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "Jsthe7wKp3vI", "outputId": "2a0bf84e-440a-4b2b-c007-051210e3ce1f" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " s1 s2\n", "0 1 3\n", "1 2 4\n", "2 3 5\n", "3 4 6" ], "text/html": [ "\n", "

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