I couldn't burn the letter, as it didn't let me have matches in my pocket:
Set the leaflet on fire with a match from my pocket. "Burn after reading"
You decide that the leaflet's secrets must remain hidden and try to set it aflame with a match from your pocket.
You search your pockets for a match to burn the leaflet, but alas, you find nothing of the sort. It seems you'll need to find another way if you wish to dispose of the evidence.
Shred the leaflet and slowly eat the pieces.
Intent on secrecy, you tear the leaflet into tiny pieces, preparing to consume them one by one.
You attempt to rip apart the leaflet, but the act baffles you—this stubborn paper refuses to tear, as if protected by some strange magic. The leaflet remains whole, defiantly intact in your hands.
put the leaflet back in the mailbox. Shake your fist to the sky and curse the rigidity of this universe.
Unable to destroy the leaflet, you decide to return it to the mailbox.
With a dramatic glare, you raise your fist to the heavens, venting your frustration at this world's strange laws. The birds and trees ignore your plight, and the universe remains as unyielding as ever.
No matter how passionately you try to shake your fist at the sky, the very concept escapes you. The universe itself seems to smirk at your futile rebellion.
The southern end of the central valley (San Joaquin region, whole central valley is outlined in red) is particularly hard-hit by groundwater depletion. Some of that storage does not come back, because the ground compacts after the groundwater is withdrawn.
They never appealed to me the way that Brooks and Puma do.
As someone who does 1000's of miles a year on foot it's firstmost about health and even if I am doing character work it is about having freedom to move first and getting the look right second.
I have usually resisted KPIs but I have them now like "Got mistaken for an animal (ex. hunters and dogs)", "Heart rate (low) for adjusted gaits", "People laughed", etc. I pass out more business cards now in a week than I used to do in three months and from a KPI perspective I'm doing about 50% of what I could.
It's pretty easy. SBERT + classical classifiers from scikit-learn, don't forget the probability calibration. I get diversity by clustering on k-Means and taking the best N/k from k=20 clusters and I also blend in about 30% random items to keep the system honest.
It's on my agenda to make a general-purpose text classifier with a "better" model (better sensitivity to word order) but I don't think a better AUC-ROC would really make a difference in my case and a recommender model can't be that accurate anyway because I'm fickle and my judgements depend on how I'm feeling and how many articles about the same subject I've seen lately.
Fact is that I should change the status of that because even though I use it everyday I've only patched it twice in the last year. It spins like a top.
Whatever you do don't screw around with fine-tuned BERT. With noisy judgements you won't really get better accuracy than BERT+SVM and there's something to say for a fast model trainer that makes a good model 100% of the time without manual intervention. I haven't seen a training recipe I can believe in for that kind of model and "catastrophic forgetting" seems to eat you alive if you have 5000+ samples. For a general classifier I am thinking of selection between
Microsoft CEO says the company doesn't have enough electricity to install all the AI GPUs in its inventory - 'you may actually have a bunch of chips sitting in inventory that I can’t plug in'
The only thing Google Glass was good for is checking e-mail. It would overheat and shutoff if you tried to record more than a few seconds of video or anything with real-time graphics.
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