# Trained model location path **URL:** \ **Category:** Uncategorized\ **Tags:** usage, ner\ **Created:** [May 4, 2022, 5:59pm UTC](https://support.prodi.gy/t/trained-model-location-path/5610 "2022-05-04T17:59:24Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![dave-espinosa](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/dave-espinosa/32/2628_2.png) [@dave-espinosa](https://support.prodi.gy/u/dave-espinosa)\ **Post date:** [May 4, 2022, 5:59pm UTC](https://support.prodi.gy/t/trained-model-location-path/5610/1 "2022-05-04T17:59:24Z")
Hello everyone. I have some experience using spaCy, however we have been recently provided of Prodigy as one of our development tools. As an exploratory exercise, I was trying to train a NER model (inside a virtual environment as a good practice) using the [corresponding command](https://prodi.gy/docs/recipes#train). More specifically, I ran the following command: `prodigy train ./test_model --ner station1_job1` The model manages to successfully train. If my understanding is correct, I should find all the model related files, at the `./test_model` directory; however (and a bit embarrased here TBH), I cannot find that directory anywhere (!) What am I missing here? How to access the files related to the model? How to "export" a model so it becomes a folder (in the same fashion than spaCy)? Thank you. ---
**Author:** ![ljvmiranda921](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ljvmiranda921/32/3197_2.png) [@ljvmiranda921](https://support.prodi.gy/u/ljvmiranda921)\ **Post date:** [May 5, 2022, 2:05am UTC](https://support.prodi.gy/t/trained-model-location-path/5610/2 "2022-05-05T02:05:03Z")
Hi @dave-espinosa , that's a bit unexpected 😕 Ideally you'd see the `test_model` folder with both `model-best` and `model-last` directories inside. Can you try running ```python prodigy train ./test_model --ner --verbose ``` And copy-paste the whole log here? You can try it out with a small sample first so that it trains fast. Under the hood, the whole `prodigy train` process also runs [`spacy train`](https://spacy.io/api/cli#train), so you should be able to achieve similar results if you went that route ---
**Author:** ![dave-espinosa](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/dave-espinosa/32/2628_2.png) [@dave-espinosa](https://support.prodi.gy/u/dave-espinosa)\ **Post date:** [May 5, 2022, 8:32pm UTC](https://support.prodi.gy/t/trained-model-location-path/5610/3 "2022-05-05T20:32:13Z")
Hello @ljvmiranda921 , I think I forgot to mention a couple of (important) details, which might be of interest: 1. `Prodigy` is currently a "tool under test" in my team, and my manager purchased it, storing it in a [Compute Engine](https://cloud.google.com/compute) VM; therefore, I don't have "owner permissions" over it. 2. For the reason explaned above, I am not "directly" running the command I mentioned in my original post, but _"emulating the super user"_. For the command you suggested for instance, what I am running looks as follows: ```python # 'super_user' was changed, due to privacy issues sudo runuser -l super_user -c 'python3 -m prodigy train ./test_model --ner station1_job1 --verbose' ``` After running the command mentioned above, the following results are obtained: ```python ========================= Generating Prodigy config ========================= ℹ Auto-generating config with spaCy ✔ Generated training config =========================== Initializing pipeline =========================== [2022-05-05 17:25:17,007] [INFO] Set up nlp object from config Components: ner Merging training and evaluation data for 1 components - [ner] Training: 386 | Evaluation: 96 (20% split) Training: 386 | Evaluation: 96 Labels: ner (4) - [ner] SOFTSKILL, SOFTWARE, SPECTOOL, HARDSKILL [2022-05-05 17:25:17,846] [INFO] Pipeline: ['tok2vec', 'ner'] [2022-05-05 17:25:17,850] [INFO] Created vocabulary [2022-05-05 17:25:17,851] [INFO] Finished initializing nlp object [2022-05-05 17:25:21,020] [DEBUG] [W033] Training a new parser or NER using a model with no lexeme normalization table. This may degrade the performance of the model to some degree. If this is intentional or the language you're using doesn't have a normalization table, please ignore this warning. If this is surprising, make sure you have the spacy-lookups-data package installed and load the table in your config. The languages with lexeme normalization tables are currently: cs, da, de, el, en, id, lb, mk, pt, ru, sr, ta, th Load the table in your config with: [initialize.lookups] @misc = "spacy.LookupsDataLoader.v1" lang = ${nlp.lang} tables = ["lexeme_norm"] [2022-05-05 17:25:22,502] [INFO] Initialized pipeline components: ['tok2vec', 'ner'] ✔ Initialized pipeline ============================= Training pipeline ============================= Components: ner Merging training and evaluation data for 1 components - [ner] Training: 386 | Evaluation: 96 (20% split) Training: 386 | Evaluation: 96 Labels: ner (4) - [ner] SOFTSKILL, SOFTWARE, SPECTOOL, HARDSKILL [2022-05-05 17:25:23,261] [DEBUG] Removed existing output directory: test_model/model-best [2022-05-05 17:25:23,263] [DEBUG] Removed existing output directory: test_model/model-last ℹ Pipeline: ['tok2vec', 'ner'] ℹ Initial learn rate: 0.001 E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE --- ------ ------------ -------- ------ ------ ------ ------ 0 0 0.00 247.44 0.98 0.66 1.93 0.01 0 200 440.69 10213.80 21.18 42.38 14.12 0.21 1 400 312.32 6208.34 35.14 49.91 27.12 0.35 1 600 528.59 5607.65 46.36 50.12 43.12 0.46 2 800 392.14 5033.03 48.00 55.46 42.31 0.48 2 1000 424.79 4397.42 49.85 54.59 45.86 0.50 3 1200 481.08 4604.98 48.25 65.65 38.14 0.48 3 1400 568.77 3875.40 51.24 58.38 45.66 0.51 4 1600 553.32 3732.43 51.73 60.56 45.15 0.52 4 1800 700.81 3609.54 50.12 62.47 41.85 0.50 5 2000 756.99 3692.40 52.24 58.37 47.28 0.52 5 2200 731.88 3032.61 50.73 62.77 42.56 0.51 6 2400 764.19 3166.53 52.29 63.10 44.64 0.52 6 2600 791.88 2730.50 53.47 64.20 45.81 0.53 7 2800 858.60 2708.93 50.59 61.91 42.76 0.51 7 3000 1051.33 2844.19 51.69 55.40 48.45 0.52 8 3200 950.03 2255.55 51.07 54.84 47.79 0.51 8 3400 1075.91 2678.61 51.75 54.20 49.52 0.52 9 3600 1201.84 2239.80 52.82 59.03 47.79 0.53 9 3800 1182.50 2467.74 52.21 56.54 48.50 0.52 10 4000 1168.55 2098.34 53.64 59.54 48.81 0.54 10 4200 1338.16 2207.73 52.91 54.46 51.45 0.53 11 4400 953.04 1652.57 52.86 58.22 48.40 0.53 11 4600 1355.55 2106.60 50.37 52.88 48.10 0.50 12 4800 1311.10 1786.16 51.93 56.44 48.10 0.52 12 5000 1292.80 1807.32 53.13 56.31 50.28 0.53 13 5200 1550.46 1641.75 52.17 55.32 49.37 0.52 14 5400 1445.59 1778.92 53.29 58.75 48.76 0.53 14 5600 1358.91 1425.84 52.04 56.48 48.25 0.52 ✔ Saved pipeline to output directory test_model/model-last ``` However, I still could not find such `test_model` directory. I have just figured out that _maybe_ than model was landing in the "super user" home directory... _which ended up being true_. With this in mind, I slightly changed the command to: ```python # 'super_user' was changed, due to privacy issues sudo runuser -l super_user -c 'python3 -m prodigy train ./my_home_directory/test_model --ner station1_job1 --verbose' ``` ...thinking this way, I was gonna obtain my **"goal result"** : ```python |-- my_home_directory | |--test_model | |--model_best | |--model_last |--super_user # don't care what happens here; don't have permissions to work here ``` Funnily enough, and after temporarily request access to `super_user`, what I ended up having, was: ```python |-- my_home_directory # No model here! |--super_user |--my_home_directory # Prodigy being 'funny' XD |--test_model |--model_best |--model_last ``` Needless to say, what I want to achieve my **"goal result"** : exporting models to a folder destination "outside Prodigy owner's 'home folder'". Is that possible? ---
**Author:** ![ljvmiranda921](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ljvmiranda921/32/3197_2.png) [@ljvmiranda921](https://support.prodi.gy/u/ljvmiranda921)\ **Post date:** [May 6, 2022, 12:39am UTC](https://support.prodi.gy/t/trained-model-location-path/5610/4 "2022-05-06T00:39:51Z")
Hi @dave-espinosa , is it because when you're using `sudo runuser -l`, the [home directory actually changes](https://man7.org/linux/man-pages/man1/runuser.1.html)? You can try inspecting the current directory you're on with `ls` just to be sure. ---
**Author:** ![dave-espinosa](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/dave-espinosa/32/2628_2.png) [@dave-espinosa](https://support.prodi.gy/u/dave-espinosa)\ **Post date:** [May 6, 2022, 10:44pm UTC](https://support.prodi.gy/t/trained-model-location-path/5610/5 "2022-05-06T22:44:28Z")
Hello @ljvmiranda921 , Thanks for your heads-up, I was aware of that; however _I was wondering if Prodigy can output a model to a directory distinct than "Prodigy owner's"?_ Best regards. ---
**Author:** ![ljvmiranda921](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ljvmiranda921/32/3197_2.png) [@ljvmiranda921](https://support.prodi.gy/u/ljvmiranda921)\ **Post date:** [May 11, 2022, 5:55am UTC](https://support.prodi.gy/t/trained-model-location-path/5610/6 "2022-05-11T05:55:40Z")
Hi, if you want to ensure that the model will output on the directory you want, perhaps it may be better to specify the absolute path. Under the hood, Prodigy resolves the directories using Python's [pathlib](https://docs.python.org/3/library/pathlib.html) module, so it shouldn't deviate much from that.