{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"4rKj_0sqHhaH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1707726851775,"user_tz":-60,"elapsed":16369,"user":{"displayName":"Luca Flammia","userId":"10941016142758317223"}},"outputId":"084509c4-1cd6-4eff-e2a4-9a87a109a319"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n","Collecting datasets\n"," Downloading datasets-2.17.0-py3-none-any.whl (536 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.6/536.6 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting evaluate\n"," Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta 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(from sympy->torch>=1.10.0->accelerate) (1.3.0)\n","Installing collected packages: pyarrow, dill, responses, multiprocess, accelerate, datasets, evaluate\n"," Attempting uninstall: pyarrow\n"," Found existing installation: pyarrow 10.0.1\n"," Uninstalling pyarrow-10.0.1:\n"," Successfully uninstalled pyarrow-10.0.1\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed accelerate-0.27.0 datasets-2.17.0 dill-0.3.8 evaluate-0.4.1 multiprocess-0.70.16 pyarrow-15.0.0 responses-0.18.0\n"]}],"source":["!pip install transformers datasets evaluate accelerate"]},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"id":"1G_FpxTh4tzq","executionInfo":{"status":"ok","timestamp":1707818040100,"user_tz":-60,"elapsed":36,"user":{"displayName":"Luca Flammia","userId":"10941016142758317223"}},"outputId":"6521deff-9d0d-4617-ae0f-04a40a547e81","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Tue Feb 13 09:53:59 2024 \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|=========================================+======================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 33C P8 8W / 70W | 0MiB / 15360MiB | 0% Default |\n","| | | N/A |\n","+-----------------------------------------+----------------------+----------------------+\n"," \n","+---------------------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=======================================================================================|\n","| No running processes found |\n","+---------------------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MM4Ot8OaHsvX","colab":{"base_uri":"https://localhost:8080/","height":372,"referenced_widgets":["b932c5c6b7aa44509877a61d834a6fae","84e0f46af4ed4d2c9c5f3b85deb24f1b","e8d26519e6b04f4586e1bba5205727fb","4c75f635d0a047339ab1bce5ba38d909","51c153fa305c484288879c70a0e183ea","6bb4b71ad1524856a6bfaca42829c4b8","876f3903c45f4060a628ebf85948d2e8","e6290a07a292444cad316c8d8839d8e0","67042e5f218243de90af0e24d471eb2d","88e8c78078b842129ae5e30174e2df58","47a5424cffcf406a989d3d48610846b3","250b643639b146a0a0a1dee85cd0e0be","4c9c71e3811e4126a0ecaccd3866d232","d861e52c9df144489416351ff92c3a91","388e923a022e40c5a81b4494d7e9c5fa","7c057e49561347f5bb0eff034aa659b6","22185e5c6edf4be4b79638b119a193e8","ae1c728bdfb040af96e8de8330faf721","94fd0586a8f04007a74bd374a0fd7256","86b5ac61575644958490a2fdfc5cbb69","f2fb1ba254bf4277817c6bb78405df23","62f1206f6a914816b6a5cae36135fb59","4ad4c9b42a1a45a4bc620304bd64e947","7916655f1799425b8cfa4f74006b71d5","2059ae9f85d54963a5d6989539bfd644","218c242b4c47461c88fc0f16b9282448","17e87b64695841faaa415cb6f6b06c88","bc6ced2827b947659c13cba2c4d912ab","be2fa53cd68a47db8bf6bd73da623cf4","c82047ed09ec43bcb198aa7dad1672a5","59cc381cbf064427b0c49ac94934bffe","f4d147f9173149a2837b90c6d6b93234","34f9839fe73c438b85060650e62fc2d4","9af4cda7b69944969cd770367ab181ea","87d3d0638fb5442a96ccd7b708be3d77","f78fb1c0f3ef4ce9bbc79ff8dbaf0bfb","4a47c5114a7442a2841ff6cfaec9729c","1e8d9254da5b43ee86a165a914dd46dd","ca8d6691be15492b9fa9b3011a378662","fda5355bed3e4d3a9c0caf63ddc5d051","12842b71923548bdb3eb766a254c8215","a067c1ce11f848b1bf5a9e44a7928732","e4365d5bdb424699a6e0dd8305ea7bda","e334b38642ad4ff5ace4fab6092e831b","5b17bb4374c6405b94aa805fec0bfaa3","31b54259535e48eb985e684bd530ed47","b1afdf03241f4dc28cc1c7979089c5d0","d15db7c6b40a4f939d24c2fb6c37b2a4","14f4a245c636451db4256196b1bf2189","d5bcc2d221514e33876c11408e36d607","e983fc811f314c449900f98d46a557f2","ef5b08f22c8b476eb237bc6865b91355","9b52c416daa4448abede75aa1399d59e","4ba070b625bf41c596d09a15e9708c9f","ee23218712f041319ea0efca314d1944","49f99e2a1fb54187807fac0c162c0626","3a517b36f5ca44f5acf409f1318ffd94","5381c35da8c84aad9e1846cf04bf0c6e","8e56f9f3d3c840e4a9d030c057f5692d","ddbb84177ce545d4bb6c7f31ad3bccc4","85366071a82c460db584d2fcf2529933","2fe1fd42161b4f48984cfc5fab0bc768","388d8c45a0054ab18d11076e93c00ba7","3eff0384344a40c99d86aa6cc348739c","77d35ed5f303411185b416a363420c00","5890b21f372d49cd9d81ad3095aaca59","aa8fdbf7dca548c18f1a6af04e118801","4d3390148428474b8617926a5b82e17e","9d79da0fe0c3420e81109ec93a4e7026","e5394bf535c24dd09f22f7b62de51a75","83cbb94736ad40afb660f1f83743a14a","71c394e1e8524d29b76fd447a2445fb4","ea4ebde02b8345e69d2cfdba75a99935","8593c2bb6cc74dd1abaefc6546441bc9","9e803035e8a44989806cc862bc98b3e7","a95e4b071b7441238de64324dcde287e","83ffe2273d494d3186b17cb39c4678b5"]},"executionInfo":{"status":"ok","timestamp":1707726863080,"user_tz":-60,"elapsed":11310,"user":{"displayName":"Luca Flammia","userId":"10941016142758317223"}},"outputId":"8b958ef0-5d2a-4532-fda7-d85b001ba6d3"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading readme: 0%| | 0.00/7.81k [00:00
Former New Orleans homicide cop Jack Robideaux (Jean Claude Van Damme) is re-assigned to Columbus, a small but violent town in Mexico to help the police there with their efforts to stop a major heroin smuggling operation into their town. The culprits turn out to be ex-military, lead by former commander Benjamin Meyers (Stephen Lord, otherwise known as Jase from East Enders) who is using a special method he learned in Afghanistan to fight off his opponents. But Jack has a more personal reason for taking him down, that draws the two men into an explosive final showdown where only one will walk away alive.

After Until Death, Van Damme appeared to be on a high, showing he could make the best straight to video films in the action market. While that was a far more drama oriented film, with The Shepherd he has returned to the high-kicking, no brainer action that first made him famous and has sadly produced his worst film since Derailed. It's nowhere near as bad as that film, but what I said still stands.

A dull, predictable film, with very little in the way of any exciting action. What little there is mainly consists of some limp fight scenes, trying to look cool and trendy with some cheap slo-mo/sped up effects added to them that sadly instead make them look more desperate. Being a Mexican set film, director Isaac Florentine has tried to give the film a Robert Rodriguez/Desperado sort of feel, but this only adds to the desperation.

VD gives a particularly uninspired performance and given he's never been a Robert De Niro sort of actor, that can't be good. As the villain, Lord shouldn't expect to leave the beeb anytime soon. He gets little dialogue at the beginning as he struggles to muster an American accent but gets mysteriously better towards the end. All the supporting cast are equally bland, and do nothing to raise the films spirits at all.

This is one shepherd that's strayed right from the flock. *\",\n"," 'label': 0}"]},"metadata":{},"execution_count":3}],"source":["imdb[\"test\"][3]"]},{"cell_type":"markdown","metadata":{"id":"aU9CbfPZIoyH"},"source":["## Data Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6teEKk76IveX"},"outputs":[],"source":["# import pandas as pd\n","\n","# imdb.set_format(type=\"pandas\")\n","# df = imdb[\"train\"][:]\n","# df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qd5o35r8JoUe"},"outputs":[],"source":["# df[\"label\"].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NaoyaZaDJTsR"},"outputs":[],"source":["# import matplotlib.pyplot as plt\n","\n","# df[\"label\"].value_counts(ascending=True).plot.bar()\n","# plt.title(\"Frequency of Classes\")\n","# plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"Fcb_w0MPJQgM"},"source":["# Tokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ctue5LVJH0tR","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["6add1d2228c64c789d7502dd1a07005d","7b0b38c605ca4e5bbb301789d008937f","0787d077e73d4b30b5cf2d33bb8d4def","7691cac525cd4b02af9216b51280d2e1","9b9adabed2674a75b10169bfec585cca","6daf9db765904c399e42f64c3c9c13fe","485e6fbe2e014578811be6cbbc63a4ad","c263a9cb17e245cda6c5d1df8249a5aa","5d2ef5511fd5401ea4d5334cd13c76ff","e2a308f5cd0649d8a11d3254dfe12c2e","09c777d3a2024eb8969473f26186b209","8f5cf59adaaf4446a088f3cf73f80b35","f9378ec229a94352916c00465708105d","60987dbbbc154d0abd4daad7d06de309","95a941c5934b48c49f0fad645b49b1fc","5c3e88917e354823badec7656b9470aa","62c8154198c54e2287caba06db55ef3f","61261be18baa45039aa2e8d84156888c","f7907a17512a4559bea7bc4c80355427","78a12bf66eb74d548b1241a57e625e0c","9daea9fef4e64a6d9bc1155e4bce66a1","fab2024e91bc431cb767fcb9e01475e2","899a499654094b12ab928f6a461d83f3","2167f7c216734fe0b3b0bf37aab5270c","d6755d0ec2774dffbca60d6c4aa64991","aacba38ddd184ac7ad870348d6f6863e","95f9c2a042634dcfa6d3e56580ac96ee","b7e8f952069045d6bc51b9be2e02e619","987a0b6fa7d34d1485cf97dc8277fe9e","85e027aa1c344436a02f0e18469bc5c9","3522014e00f9441ea781098bfe5511dd","b304784105194571af2ac450fe5e17b2","01b1a8baace9410094f908e3ee83d958","25591b11b88a466780cd5e61ce3332e1","e8049275040d41748481b22cc93b16b0","580238f7c20b4cd680a5eff1e8df17a8","f8c93abcd7c44aa6bad2c0ed559a7b71","a6e2c9a98fac42848be28fc85d96bb13","9b48c43d0a4a443d85cf1cfe3dc3bec4","25533482b2964b5ca5e175b44383a75b","da6087c996ca43ffb7cc890d1cf08411","45229eba61044a4ca3817639f232283e","d742fb5869e24ad4b1e79a67d89da743","bf84bb8e038c4cc8acf9d68fca347dd2"]},"executionInfo":{"status":"ok","timestamp":1707726870445,"user_tz":-60,"elapsed":7389,"user":{"displayName":"Luca Flammia","userId":"10941016142758317223"}},"outputId":"63c034cc-8ce1-44f3-a9c0-30622e20d343"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/28.0 [00:00 "],"text/html":["\n","
\n"," \n"," \n"," [3126/3126 52:32, Epoch 2/2]\n","
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EpochTraining LossValidation LossAccuracy
10.2273000.2471060.912240
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