pushing files to the repo from the example!

README.md

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+---
+license: mit
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- text-classification
+---
+
+# Model description
+
+This is a multinomial naive Bayes model trained on 20 new groups dataset. Count vectorizer and TFIDF vectorizer are used on top of the model.
+
+## Intended uses & limitations
+
+This model is not ready to be used in production.
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter      | Value                                                                                  |
+|---------------------|----------------------------------------------------------------------------------------|
+| memory              |                                                                                        |
+| steps               | [('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())] |
+| verbose             | False                                                                                  |
+| vect                | CountVectorizer()                                                                      |
+| tfidf               | TfidfTransformer()                                                                     |
+| clf                 | MultinomialNB()                                                                        |
+| vect__analyzer      | word                                                                                   |
+| vect__binary        | False                                                                                  |
+| vect__decode_error  | strict                                                                                 |
+| vect__dtype         | <class 'numpy.int64'>                                                                  |
+| vect__encoding      | utf-8                                                                                  |
+| vect__input         | content                                                                                |
+| vect__lowercase     | True                                                                                   |
+| vect__max_df        | 1.0                                                                                    |
+| vect__max_features  |                                                                                        |
+| vect__min_df        | 1                                                                                      |
+| vect__ngram_range   | (1, 1)                                                                                 |
+| vect__preprocessor  |                                                                                        |
+| vect__stop_words    |                                                                                        |
+| vect__strip_accents |                                                                                        |
+| vect__token_pattern | (?u)\b\w\w+\b                                                                          |
+| vect__tokenizer     |                                                                                        |
+| vect__vocabulary    |                                                                                        |
+| tfidf__norm         | l2                                                                                     |
+| tfidf__smooth_idf   | True                                                                                   |
+| tfidf__sublinear_tf | False                                                                                  |
+| tfidf__use_idf      | True                                                                                   |
+| clf__alpha          | 1.0                                                                                    |
+| clf__class_prior    |                                                                                        |
+| clf__fit_prior      | True                                                                                   |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 {color: black;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 pre{padding: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable {background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-item {z-index: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:only-child::after {width: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),(&#x27;clf&#x27;, MultinomialNB())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" type="checkbox" ><label for="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),(&#x27;clf&#x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" type="checkbox" ><label for="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69b80eb1-41d4-421a-9875-a9e95faa6d45" type="checkbox" ><label for="69b80eb1-41d4-421a-9875-a9e95faa6d45" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfTransformer</label><div class="sk-toggleable__content"><pre>TfidfTransformer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" type="checkbox" ><label for="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric   | Value   |
+|----------|---------|
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import pickle
+with open(pkl_filename, 'rb') as file:
+    clf = pickle.load(file)
+```
+
+</details>
+
+
+
+
+# Model Card Authors
+
+This model card is written by following authors:
+
+merve
+
+# Model Card Contact
+
+You can contact the model card authors through following channels:
+[More Information Needed]
+
+# Citation
+
+Below you can find information related to citation.
+
+**BibTeX:**
+```
+bibtex
+@inproceedings{...,year={2020}}
+```

config.json

@@ -0,0 +1,18 @@
+{
+    "sklearn": {
+        "environment": [
+            "scikit-learn=1.0.2"
+        ],
+        "example_input": {
+            "data": [
+                "From: [email protected] (Brian Kantor)\nSubject: Re: HELP for Kidney Stones ..............\nOrganization: The Avant-Garde of the Now, Ltd.\nLines: 12\nNNTP-Posting-Host: ucsd.edu\n\nAs I recall from my bout with kidney stones, there isn't any\nmedication that can do anything about them except relieve the pain.\n\nEither they pass, or they have to be broken up with sound, or they have\nto be extracted surgically.\n\nWhen I was in, the X-ray tech happened to mention that she'd had kidney\nstones and children, and the childbirth hurt less.\n\nDemerol worked, although I nearly got arrested on my way home when I barfed\nall over the police car parked just outside the ER.\n\t- Brian\n",
+                "From: [email protected] (David Rind)\nSubject: Re: Candida(yeast) Bloom, Fact or Fiction\nOrganization: Beth Israel Hospital, Harvard Medical School, Boston Mass., USA\nLines: 37\nNNTP-Posting-Host: enterprise.bih.harvard.edu\n\nIn article <[email protected]>\n [email protected] writes:\n>are in a different class.  The big question seems to be is it reasonable to \n>use them in patients with GI distress or sinus problems that *could* be due \n>to candida blooms following the use of broad-spectrum antibiotics?\n\nI guess I'm still not clear on what the term \"candida bloom\" means,\nbut certainly it is well known that thrush (superficial candidal\ninfections on mucous membranes) can occur after antibiotic use.\nThis has nothing to do with systemic yeast syndrome, the \"quack\"\ndiagnosis that has been being discussed.\n\n\n>found in the sinus mucus membranes than is candida.  Women have been known \n>for a very long time to suffer from candida blooms in the vagina and a \n>women is lucky to find a physician who is willing to treat the cause and \n>not give give her advise to use the OTC anti-fungal creams.\n\nLucky how?  Since a recent article (randomized controlled trial) of\noral yogurt on reducing vaginal candidiasis, I've mentioned to a \nnumber of patients with frequent vaginal yeast infections that they\ncould try eating 6 ounces of yogurt daily.  It turns out most would\nrather just use anti-fungal creams when they get yeast infections.\n\n>yogurt dangerous).  If this were a standard part of medical practice, as \n>Gordon R. says it is, then the incidence of GI distress and vaginal yeast \n>infections should decline.\n\nAgain, this just isn't what the systemic yeast syndrome is about, and\nhas nothing to do with the quack therapies that were being discussed.\nThere is some evidence that attempts to reinoculate the GI tract with\nbacteria after antibiotic therapy don't seem to be very helpful in\nreducing diarrhea, but I don't think anyone would view this as a\nquack therapy.\n-- \nDavid Rind\[email protected]\n",
+                "From: [email protected] ()\nSubject: Re: centi- and milli- pedes\nOrganization: Iowa State University, Ames IA\nLines: 37\n\nIn <[email protected]> [email protected] writes:\n\n>In article <[email protected]>, [email protected] (G T Clark) writes:\n>> [email protected] (Rebecca Snyder) writes:\n>> \n>>>Does anyone know how posionous centipedes and millipedes are? If someone\n>>>was bitten, how soon would medical treatment be needed, and what would\n>>>be liable to happen to the person?\n>> \n>>>(Just for clarification - I have NOT been bitten by one of these,  but my\n>>>house seems to be infested, and I want to know 'just in case'.)\n>> \n>>>Rebecca\n>> \n>> \n>> \tMillipedes, I understand, are vegetarian, and therefore almost\n>> certainly will not bite and are not poisonous. Centipedes are\n>> carnivorous, and although I don't have any absolute knowledge on this, I\n>> would tend to think that you're in no danger from anything but a\n>> concerted assault by several million of them.\n>> \n>> \t\t\tG.\n>Not sure of this but I think some millipedes cause a toxic reaction (sting?\n>So I would not assume that they are not dangerous merely on the basis of \n>vegetarianism, after all wasps are vegetarian too.\n>dnc.\n\nAs a child i can remember picking up a centipede and getting a rather painful \nsting, but it quickly subsided. Much less painful compared to a bee sting. \nCentipedes have a poison claw (one of the front feet) to stun their prey, but\nin my single experience it did not have a lot of \"bite\" to it.\n\nA.\n\n\n\n\n"
+            ]
+        },
+        "model": {
+            "file": "model.pkl"
+        },
+        "task": "text-classification"
+    }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fce5d3f7326bb1a00d0047f6b61b34553685919dd4e95b5e005e71e94f7deb5
+size 3584230