sklearn-crfsuite | scikit-learn inspired API for CRFsuite | Machine Learning library

I have exactly the same problem as mentioned in PIP install rasa-x takes forever. In the Rasa installation guide they say, you have to create an environment first. Everytime I do: conda create --name rasa python==3.7.6 it automatically downloads pip-20.3.3. If I now try the pip install --upgrade pip==20.2 command it shows the following error: Error. What did I do wrong? Thanks for the help!

**Update: python -m pip install --upgrade pip==20.2 worked, but now there is another problem when trying to install Rasa-X:Rasa-X installation error

here is the code

I successfully installed scikit-learn 0.23.1 with pip.

When I tried this code:

I'm trying to create an annotations prediction model, following the tutorial here, but my model doesn't learn anything. Here is a sample of my training data and labels:

[{'bias': 1.0, 'word.lower()': '\nreference\nissue\ndate\ndgt86620\n4\n \n19-dec-05\nfalcon\n7x\ntype\ncertification\n27_4-100\nthis\ndocument\nis\nthe\nintellectual\nprop...nairbrakes\nhandle\nposition\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n0\ntable\n1\n:\nairbrake\ncas\nmessages\n', 'word[-3:]': 'es\n', 'word[-2:]': 's\n', 'word.isupper()': False, 'word.istitle()': False, 'word.isdigit()': False, 'postag': 'POS', 'postag[:2]': 'PO', 'w_emb_0': 0.03418987928976114, 'w_emb_1': 0.617338281 1066742, 'w_emb_2': 0.004420982990809508, 'w_emb_3': 0.08293022662242588, 'w_emb_4': 0.22162269482070363, 'w_emb_5': 0.4334545347397811, 'w_emb_6': 0.7844891779932379, 'w_emb_7': 0.028043262790094503, 'w_emb_8': 0.5233847386564157, 'w_emb_9': 0.9685677133128328, 'w_em b_10': 0.19379126558708126, 'w_emb_11': 0.2809608896964926, 'w_emb_12': 0.384759230815804, 'w_emb_13': 0.15385904662767336, 'w_emb_14': 0.5206500040610533, 'w_emb_15': 0.009148526006733215, 'w_emb_16': 0.5894118695171416, 'w_emb_17': 0.7356989708459056, 'w_emb_18': 0. 5576774100159024, 'w_emb_19': 0.2185294430010376, 'BOS': True, '+1:word.lower()': 'reference', '+1:word.istitle()': False, '+1:word.isupper()': True, '+1:postag': 'POS', '+1:postag[:2]': 'PO'}, {'bias': 1.0, 'word.lower()': 'reference', 'word[-3:]': 'NCE', 'word[-2:]' : 'CE', 'word.isupper()': True, 'word.istitle()': False, 'word.isdigit()': False, 'postag': 'POS', 'postag[:2]': 'PO', 'w_emb_0': -0.390038, 'w_emb_1': 0.30677223, 'w_emb_2': -1.010975, 'w_emb_3': 0.3656154, 'w_emb_4': 0.5319459, 'w_emb_5': 0.45572615, 'w_emb_6': -0.4 6090943, 'w_emb_7': 0.87250936, 'w_emb_8': 0.036648277, 'w_emb_9': -0.3057043, 'w_emb_10': 0.33427167, 'w_emb_11': -0.19664396, 'w_emb_12': -0.64899784, 'w_emb_13': -0.1785065, 'w_emb_14': -0.117423356, 'w_emb_15': 0.16247013, 'w_emb_16': 0.11694676, 'w_emb_17': -0.30 693895, 'w_emb_18': -1.0026807, 'w_emb_19': 0.9946743, '-1:word.lower()': '\nreference...n \n \n \n \n \n \n \n \n0\ntable\n1\n:\nairbrake\ncas\nmessages\n', '-1:word.istitle()': False, '-1:word.isupper()': False, '-1:postag': 'POS', '-1:postag[:2]': 'PO', '+1:word.lower()': 'issue', '+1:word.istitle()': False, '+1:word. isupper()': True, '+1:postag': 'POS', '+1:postag[:2]': 'PO'}, {'bias': 1.0, 'word.lower()': 'issue', 'word[-3:]': 'SUE', 'word[-2:]': 'UE', 'word.isupper()': True, 'word.istitle()': False, 'word.isdigit()': False, 'postag': 'POS', 'postag[:2]': 'PO', 'w_emb_0': -1.220 4882, 'w_emb_1': 0.8920707, 'w_emb_2': -3.8380668, 'w_emb_3': 1.5641377, 'w_emb_4': 2.1918254, 'w_emb_5': 1.8509868, 'w_emb_6': -2.0664182, 'w_emb_7': 3.1591077, 'w_emb_8': -0.33126026, 'w_emb_9': -1.4278139, 'w_emb_10': 0.9291533, 'w_emb_11': -0.6761407, 'w_emb_12': -2.9582167, 'w_emb_13': -0.5395561, 'w_emb_14': -0.8363763, 'w_emb_15': 0.25568742, 'w_emb_16': 0.4932978, 'w_emb_17': -1.6198335, 'w_emb_18': -4.183924, 'w_emb_19': 4.281094, '-1:word.lower()': 'reference', '-1:word.istitle()': False, '-1:word.isupper()': True, '-1:p ostag': 'POS', '-1:postag[:2]': 'PO', '+1:word.lower()': 'date', '+1:word.istitle()': False, '+1:word.isupper()': True, '+1:postag': 'POS', '+1:postag[:2]': 'PO'}...]
y_train = ['O', 'O', 'O'...'I-data-c-a-s_message-type'....'B-data-c-a-s_message-type']

and here is the model definition and training:

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I'm using the rasa nlu with the supervised_embedding pipeline, and I am trying to train my models. On my local machine, I can train without any issues. When I try to train the models on my server, I am getting the following error:

i try to install a package sklearn-crfsuite https://pypi.org/project/sklearn-crfsuite/#files on my working computer in windows, where I do not have admin rights. Besides the root enviorment I already created my own enviorment following called: Test.

Normally I use anacando navigator to install new packages, there everything works fine, but this package is not in anaconda navigator, so I am opening the anaconda prompt /conda prompt to install in manually. Here the problem starts.

I start by choosing the right enviorment in the command line: activate Test

I installed pip and scikit already and have the python version 3.6.8. So I try to run the following command: pip install sklearn-crfsuite

And I get the error: Could not find a version that satisfies the requirement sklearn-crfsuite Error: no matching distrubation found for sklearn-crfsuite

I am performing NER using sklearn-crfsuite. I am trying to report back on an entity mention by entity mention case as a true positive (both prediction and expected correct even if no entity), false positive (prediction says yes, expected no) or false negative (prediction says no, expected yes).

I cannot see how to get anything other than tag/token based summary statistics for NER performance.

I would be OK with a different way of grouping entity mentions such as: correct, incorrect, partial, missing, spurious. I can write a whole bunch of code around it myself to try to accomplish this (and might have to), but there has to be a single call to get this info?

Here are some of the calls that are being made to get the summary statistics:

I am in the process of migrating a Django app from Heroku to Elastic Beanstalk. It is working fine in Heroku as is.

I am getting the error Your requirements.txt is invalid. Snapshot your logs for details. When I dive into the eb-activity.log I see the failure seems to be related to atlas and scipy. I don't understand why requirements.txt is invalid on aws but valid on heroku. Insight into what is causing this error and how to remedy would be greatly appreciated.

My eb-activity.log