{"cells":[{"cell_type":"markdown","source":["# **Language Translation**"],"metadata":{"id":"MY5EA3RRGumY"}},{"cell_type":"markdown","source":["This is the code of language translation from english to french. I have done a fine tuning of a dataset taken from huggingface on a pre trained model from huggingface only. The details of each cell code is given above that cell or in comments. "],"metadata":{"id":"sQhvGL7IHsY_"}},{"cell_type":"markdown","source":["First we will connect the GPU so as to run our program. GPU is very useful to run programs which have very large datasets."],"metadata":{"id":"tgdkWb8zG-cF"}},{"cell_type":"code","source":["!nvidia-smi #checking whether GPU is working or not"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fet3K5GfHHpQ","executionInfo":{"status":"ok","timestamp":1692193558563,"user_tz":-330,"elapsed":10,"user":{"displayName":"Suyash Shirish Gahankari","userId":"03254191982788877845"}},"outputId":"bb5bb8f2-7e6f-434f-e3fa-369833caec8b"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Wed Aug 16 13:46:04 2023 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 65C P8 12W / 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","source":["! pip install -q transformers accelerate sentencepiece gradio datasets evaluate sacrebleu\n","#installing all the necessary libraries"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UF0lJfd5HFXs","executionInfo":{"status":"ok","timestamp":1692193585491,"user_tz":-330,"elapsed":26932,"user":{"displayName":"Suyash Shirish Gahankari","userId":"03254191982788877845"}},"outputId":"c38b1903-ec56-4b97-f2bd-c73f1cffd7d9"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m32.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.0/20.0 MB\u001b[0m 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languages."],"metadata":{"id":"-qSxRNqSIk_2"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281,"referenced_widgets":["22bcd0de966d4cb5bac337af42d66e0a","00969734af0a483bb653388c03371e30","690e162651c647a48401f2116d9c0a87","676c7aa802224adbb84701fa267b9ac7","cfed60dd3ed74bb7b48cacc0fd1050c2","dd5fcb2eccba468fa61d9bec549ff566","294935b56ac74d17b912a92c4d986a0e","3ee786d6fd8a488d8e85bbd1a7db33ab","84a80f90fa274d77b67ef23dd8ab667c","0b365fd4f68945de8537abc728a17a2a","5d4f056e3b2a433ca097adc9f27742af","1d343cc64ac14e24b7fb8be89dce7b81","d991be5c17e3463f9594361134992112","3568ff949a244718b2664595bb00f558","98a088bc290d4e4ebc8f2c789a1bb18d","8dfe395f9a39412f86cdaaec7e0e4c75","f2d6b74df93b4966b570c902bef631cc","74032a1241fe4f1fbcb45e727827f13f","148252f08c4048d29c2dc8decc00117b","db954ce91fba47338913ec58ea37efbc","3fd124d8e7684e178836533a58529e0e","dd0f75907ebe4fe2afa2f8a5b948641c","9b09b999d6a449ac8d1f89aad3c8edbd","d73b40fa1e5448d786a0586bb92af525","3e5918d902d34bb1b46a2d7ecb0b0903","0863747d6a494fafbcd2a868853a2fed","3f6bd35cad0e464b883c1d44f00acc32","e37a9b756c6f42a099cad27593fc8666","bb52f273142b4ddc854f3c84b64d23ce","1b8ed6cfdafe4359bdee4ec8c7f975e7","cbf8dcb1e90d4e1381a0b8c95531e210","280f89db66534fc2a835bd507e063759","eec5d4273bd64ac787d9416feb2e67d9","89e8cafe95a64d5b8ba839c5dd7f9ac9","28cb6c3155e8485ab421371201404ac9","747003774b6e406eb851f0058e403eb0","2d03f541abd94b26bdd82e713427d8c0","afa81bdfc0dd4532b7758ec6e39f8322","45bc50c516d749deb43cdd082c5ee1c2","db361ce12736438d86a6d46420853b69","396bfeeaede0475e94576f8d66497ed5","bda1922c4935447bbfb2f639d577bbb5","94ed17e677b14ab288412129d10155da","f9f69b32069948a4a9e0db5b4b5718c0","b766fdc8956b4a9fa49818f715fe4d47","542f280b944c4942b9bb69defd894014","f159b988fd634469b2b319b5285584d6","74c907575dd24ba9b3e83fe1a7ecf7ef","4560693d445945148727bbaabef7d449","4a48a7648e374b68878a719044f5e115","cb52a841e10e4f4b8fca5ac8de94d723","88ae98f9a4574f338b73345185ed0db5","40484d37eff54d699a89e19e663ca2a5","276095aa516b451aaac8f4f2fbbd9095","084f913d48724465a0d8c4886d8bb2fb"]},"executionInfo":{"elapsed":17707,"status":"ok","timestamp":1692193603192,"user":{"displayName":"Suyash 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StepTraining Loss
5001.365600
10001.226100
15001.155900
20001.108200
25001.084700
30001.032800
35001.020600
40001.009300
45000.994800
50000.972400
55000.920800
60000.868800
65000.847700
70000.862200

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\n"," \n"," \n"," [15765/15765 52:21, Epoch 3/3]\n","
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StepTraining Loss
5001.365600
10001.226100
15001.155900
20001.108200
25001.084700
30001.032800
35001.020600
40001.009300
45000.994800
50000.972400
55000.920800
60000.868800
65000.847700
70000.862200
75000.847000
80000.845700
85000.854800
90000.845600
95000.847600
100000.847900
105000.834000
110000.762300
115000.764200
120000.767200
125000.764900
130000.770000
135000.756800
140000.756600
145000.762600
150000.766800
155000.750800

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