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Fine-tuning a Transformer for text classification

I have found myself often enough trying to classify a text that I decided to be lazy and write a solution that I could always rely on.

The following script is an example on how to pre-train a HuggingFace transformer for text classification. This example uses only two classes, identifying whether the sentiment of a text is positive or negative, but nothing stops you from adding more classes as long as you predict just one.

There are no difficult imports that I can tell - I think you can install all the required libraries with the command

pip install transformers datasets evaluate nltk

but I'll double check just in case.

Update: I have changed the script to extract the function get_dataset_from_json_files(). You can use this function as is to replace the calls to get_data(). I also updated the prediction code to give an example of how to make predictions for a lot of texts at once.

import json
import numpy as np
import os
import random
import tempfile
# These are all HuggingFace libraries
from datasets import load_dataset
import evaluate
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification,\
     TrainingArguments, Trainer, TextClassificationPipeline
# KeyDataset is a util that will just output the item we're interested in.
from transformers.pipelines.pt_utils import KeyDataset
# Library for importing a sentiment classification dataset.
# Only here for demo purposes, as you would use your own dataset.
from nltk.corpus import movie_reviews

# The BERT tokenizer is always the same so we declare it here globally.
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")

def get_data():
    """ Returns a dataset built from a toy corpora.

        An object containing all of our training, validation, and test data.
        This object stores all the information about a text, namely, the raw
        text (which is stored both as text and as numeric tokens) and the
        proper class for every text.

    If you have texts in this format already then you can skip most of this
    function and simply jump to the `get_dataset_from_files` function.

    Most of this function is taken from the code in
    Note that that link is very confusing at times, so if you read that code
    remember to be patient.
    # We read all of our (toy) data from NLTK. The exception catches the case
    # where we haven't downloaded it yet and downloads it.
        all_ids = movie_reviews.fileids()
    except LookupError:
        import nltk
        all_ids = movie_reviews.fileids()
    # We identify texts by their ID. Given that we have those IDs now, we
    # proceed to shuffle them and assign each one to a specific split.
    # Note that we fix the random seed to ensure that the datasets are always
    # the same across runs. This is not strictly necessary after creating a
    # dataset object (because we want to reuse it), but it is useful if you
    # delete that dataset and want to recreate it from scratch.
    num_records = len(all_ids)
    # This dictionary holds the name of the temporay files we will create.
    tmp_files = dict()
    # We assigne the IDs we read before to their data splits.
    # Note that we use a fixed 80/10/10 fixed split.
    for split in ['train', 'val', 'test']:
        if split == 'train':
            split_ids = all_ids[:int(0.8 * num_records)]
        elif split == 'val':
            split_ids = all_ids[int(0.8 * num_records):int(0.9 * num_records)]
            split_ids = all_ids[int(0.9 * num_records):]
        all_data = []
        # We read the data one record at the time and store it as dictionaries.
        for text_id in split_ids:
            # We read the text.
            text = movie_reviews.raw(text_id)
            # We read the class and store it as an integer.
            if movie_reviews.categories(text_id)[0] == 'neg':
                label = 0
                label = 1
            all_data.append({'id': text_id, 'text': text, 'label': label})
        # Step two is to save this data as a list of JSON records in a temporary
        # file. If you use your own data you can simply generate these files as
        # JSON from scratch and save yourself this step.
        # Note that we also save the name of the temporary file in a dictionary
        # so we can re-read it later.
        tmp_file = tempfile.NamedTemporaryFile(delete=False)
        tmp_files[split] = tmp_file.name

    # Step three is to create a data loader that will read the data we just
    # saved to disk.
    dataset = get_dataset_from_json_files(tmp_files['train'], tmp_files['val'], tmp_files['test'])

    # Remove the temporary files and return the tokenized dataset.
    for _, filename in tmp_files.items():
    return dataset

def get_dataset_from_json_files(train_file, val_file=None, test_file=None):
    """ Given a set of properly-formatted files, it reads them and returns
    a Dataset object containing all of them.

    train_file : str
        Path to a file containing training data.
    val_file : str
        Path to a file containing validation data.
    test_file : str
        Path to a file containing test data.

        An object containing all of our training, validation, and test data.
        This object stores all the information about a text, namely, the raw
        text (which is stored both as text and as numeric tokens) and the
        proper class for every text.

    IMPORTANT: the first time you run this code, the resulting dataset is saved
    to a temporary file. The console will tell you where it is (in Linux it is
    /home/<user>/.cache/huggingface/datasets/...). This temporary location is
    used in all subsequent calls, so if you change your dataset remember to
    remove this cached file first!

    This function is a thin wrapper around the `load_dataset` function where
    we hard-coded the file format to use JSON.
    If you don't want to read that function's documentation, it is enough to
    provide files whose content is simply a JSON list of dictionaries, like so:
    [{'id': '1234', 'text': 'My first text', 'label': 0}, {'id': '5678', 'text': 'My second text', 'label': 1}]
    files_dict = {'train': train_file}
    if val_file is not None:
        files_dict['val'] = val_file
    if test_file is not None:
        files_dict['test'] = test_file
    dataset = load_dataset('json', data_files=files_dict)

    # Tokenize the data
    def tokenize_function(examples):
        return tokenizer(examples['text'], padding='max_length', truncation=True)
    tokenized_dataset = dataset.map(tokenize_function, batched=True)

    # Return the tokenized dataset.
    return tokenized_dataset

if __name__ == '__main__':
    # In which mode we want to use this script
    # 'train': start from a pre-trained model, fine-tune it to a dataset, and then
    #          save the resulting model
    # 'test': evaluate the performance of a fine-tuned model over the test data.
    # 'predict': make predictions for unseen texts.
    mode = 'predict'
    assert mode in ['train', 'test', 'predict'], f'Invalid mode {mode}'
    # We define here the batch size and training epochs we want to use.
    batch_size = 16
    epochs = 10
    # Where to save the trained model.
    trained_model_dir = 'training_output_dir'
    if mode == 'train':
        # Train mode. Code adapted mostly from:
        # https://huggingface.co/docs/transformers/training#train-with-pytorch-trainer.
        # Collect the training data
        inputs = get_data()
        # Since we are starting from scratch we download a pre-trained bert model
        # from HuggingFace. Note that we are hard-coding the number of classes here!
        model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
        # Define training arguments
        # You can define a loooot more hyperparameters - see them all in
        # https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
        training_args = TrainingArguments(output_dir=trained_model_dir,
        # Define training evaluation metrics
        metric = evaluate.load('accuracy')

        def compute_metrics(eval_pred):
            logits, labels = eval_pred
            predictions = np.argmax(logits, axis=-1)
            return metric.compute(predictions=predictions, references=labels)
        # Define the trainer object and start training.
        # The model will be saved automatically every 500 epochs.
        trainer = Trainer(model=model,
    elif mode == 'test':
        # Perform a whole run over the validation set.
        # This code is adapted mostly from:
        # https://huggingface.co/docs/evaluate/base_evaluator
        # Some useful information also here:
        # https://huggingface.co/docs/datasets/metrics

        # Collect the validation data
        inputs = get_data()
        # Create our evaluator
        task_evaluator = evaluate.evaluator("text-classification")

        # Use the model we trained before to predict over the validation data.
        # Note that we are providing the name of the classes by hand. We are not
        # using these labels here, but it is useful for the prediction branch.
        model = DistilBertForSequenceClassification.from_pretrained(f'./{trained_model_dir}/checkpoint-1000/',
                                                                    id2label={0: 'negative', 1: 'positive'})
        # Define the evaluation parameters. We use the common text evaluation
        # measures, but there are plenty more.
        eval_results = task_evaluator.compute(
            metric=evaluate.combine(['accuracy', 'precision', 'recall', 'f1']),
            label_mapping={"negative": 0, "positive": 1}
        # `eval_results` is a dictionary with the same keys we defined in
        # the `metric` parameters, plus some time measures.
    elif mode == 'predict':
        # Make predictions for individual texts.
        # Same as above, we use the model we trained before to predict a single text.
        model = DistilBertForSequenceClassification.from_pretrained(f'./{trained_model_dir}/checkpoint-1000/',
                                                                    id2label={0: 'negative', 1: 'positive'})
        # We build a text classification pipeline.
        # Note that `top_k=None` gives us probabilities for every class while
        # `top_k=1` returns values for the best class only.
        # Inspired on https://discuss.huggingface.co/t/i-have-trained-my-classifier-now-how-do-i-do-predictions/3625
        pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer,
                                          batch_size=batch_size, top_k=None)
        sequences = ['Hello, my dog is cute', 'Hello, my dog is not cute']
        predictions = pipe(sequences)
        # The above print shows something like:
        # [[{'label': 'positive', 'score': 0.574},
        #   {'label': 'negative', 'score': 0.426}],
        #  [{'label': 'negative', 'score': 0.868},
        #  {'label': 'positive', 'score': 0.132}]]

        # For a more heavy-duty approach we now classify an entire dataset.
        inputs = get_data()
        test_inputs = inputs['test']
        outputs = []
        # Note that 'text' is the key we defined in the `get_data` function.
        for out in pipe(KeyDataset(test_inputs, 'text'), batch_size=batch_size, truncation=True, max_length=512):
        # Now that we collected all outputs we massage them a little bit for
        # a more friendly format. For every prediction we will print a line like:
        # 'pos/cv093_13951.txt: positive (0.996)'
        for i in range(len(test_inputs)):
            id = test_inputs[i]['id']
            pred_label = outputs[i][0]['label']
            pred_prob = outputs[i][0]['score']
            print(f'{id}: {pred_label} ({pred_prob})')

Older Posts

Thoughts on ChatGPT, LLMs, and search engines

Now that everyone is talking about Large Language Models (LLMs) in general and ChatGPT in particular I thought I would share a couple thoughts I've been having about this technology that I haven't seen anywhere else. But first, let's talk about chess.

Chess is a discipline notorious for coming out on top when the robots came to take its lunch. Once it became clear that even the humblest of PCs can play better than a World Chess Champion, the chess community started using these chess engines to improve their games and learn new tactics. Having adopted these engines as a fact of life, chess is as popular as ever (if not more). Sure, a computer can do a "better" job on a fraction of the time, but who cares? It's not like there's an unmet economic need for industrial-strength chess players.

In contrast, one field that isn't doing as well is digital art. With the advent of diffusion models like Midjourney and Stable Diffusion many artists are worried that their livelihoods are now at stake and are letting the world know, ranging from comics (I like this one more but this one hits closer to home) all the way to class action lawsuits. My quick take is that these models are here to stay and that they won't necessarily destroy art but they will probably cripple the art business the same way cheap restaurants download food pictures from the internet instead of paying a professional photographer.

LLMs are unusual in that sense because they aren't disrupting a field as much as a means of communication. "Chess player" is something one does, and so is "artist"1. But "individual who uses language" is on a different class altogether, and while some of the effects of these technologies are easy to guess, others not so much.

Random predictions

What are we likely to see in the near future? The easiest prediction is a rise in plagiarism. Students are already using ChatGPT to generate essays regardless of whether the output makes sense or not. And spam will follow closely behind: we are already awash in repost spam and ramblings disguised as recipes, but once people start submitting auto-generated sci-fi stories to magazines we will see garbage in any medium that offers even the slightest of rewards, financial or not. Do you want a high-karma account on Reddit to establish yourself as not-a-spammer and use it to push products? Just put your payment information here and the robots will comment for you. No human interaction needed2.

What I find more interesting and likely more disruptive is the replacement of search engines with LLMs. If I ask ChatGPT right now about my PhD advisor I get an answer - this particular answer happens to be all wrong3, but let's pretend that it's not 4. This information came from some website, but the system is not telling me which one. And here there's both an opportunity and a risk. The opportunity is the chance of cutting through the spam: if I ask for a recipe for poached eggs and I get a recipe for poached eggs then I no longer have to waddle through long-winded essays on how poached eggs remind someone of evening at their grandma's house. On the other hand, this also means that all the information we collectively placed on the internet would be used for the profit of some company without even the meagre attributions we currently get.

On the long tail, and entering into guessing territory, it would be tragic if people started writing like ChatGPT. These systems have a particular writing style composed of multiple short sentences and it's not hard to imagine that young, impressionable people may start copying this output once it is widespread enough. This has happened before with SMS, so I don't see why it couldn't happen again.

Pointless letters and moving forward

One positive way to move forward would be to accept that a lot of our daily communication is so devoid of content that even a computer with no understanding of the real world can do it and work on that.

When I left my last job I auto-generated a goodbye e-mail with GPT-3, and the result was so incredibly generic that no one would have been able to learn anything from it. On the other hand, I doubt anyone would have noticed: once you've read a hundred references to "the good memories" you no longer stop to wonder whether there were any good memories to begin with. I didn't send that auto-generated e-mail. In fact, I didn't send anything: I had already said goodbye in person to the people that knew me and there was no reason to say anything else. The amount of information that was conveyed was exactly the same, but my solution wasted less of other people's time.

Maybe this is our opportunity to freshen up our writing and start writing interestingly, both in form (long sentences for the win!) and in content. The most straightforward solution would be cursing: these models have to be attractive to would-be investors they are strictly programmed not to use curse words and NSFW content (I just tried). So there's a style that no AI will be copying in the near future.


  1. Note that this is a simplification for the sake of the argument. As someone who often said "being a programmer is both what I do and what I am" I am aware that "artist" (like so many other professions) isn't just a job but also a way of looking at the world.
  2. I have noticed that people on Reddit will upvote anything without reading it first, so this is not a high bar to clear.
  3. The answer mashed together several researchers into one. One could argue that I got more researchers per researcher, which is definitely a take.
  4. The answer doesn't have to be correct - all it takes is for the person using the system to believe that the answer is correct, something we are already seeing despite the overwhelming evidence to the contrary.

Smartening up, Part II

In my previous entry about home automation I mentioned that I was ready to start plugging things into other things and connecting everything to a central computer running OpenHab. This is the story about how everything went wrong.


Things starting going badly from the get go when I discovered that whatever "vectoring" is it has rendered my DSL router obsolete. Only two (available) modems had the right technology: a TP-Link TD-W9960v and a Fritz!Box 7530 AX. I should have gone with TP-Link, but alas, I did not: I decided against the TP-Link because it was not supported by OpenWRT, but failed to notice that the other one was not supported either.

The reason I regret my choice is because the Fritz!Box router provides DSL, Telephone, Wifi, WLAN Mesh, Media server, NAS, some type of IoT compatibility, it updates automatically, and who knows what else out of the box. I have never owned any other device with such a large attack surface, and yet I am expected to put all of my data in it and then plug it to the internet? Yeah, no. I disabled everything instead, plugging all of my devices to my old router (which uses dd-wrt after OpenWrt dropped its support for it), relegated the Fritz!Box to a mere modem, and added every network access restriction I know to keep the later from entering my internal network.

Speaking of my old router, setting up sub-networks in dd-wrt was like pulling teeth thanks to my favorite internet problem: documentation that still refers to older, deprecated versions of software that nonetheless rank stupidly high in search engines because they have been around forever. In my case, all links pointed to this complicated method that has been improved by this much simpler method. Do you know how frustrating it is to debug network errors when bad configuration means you can no longer access the thing you are debugging? Because I do.

Smart lights

This is the only part that worked as intended, only to realize that what I thought I wanted is not what I actually needed.

I configured the lights to use their own Guest WiFi network that, once I'm done, will be disconnected from the internet entirely. That part worked well, and in fact I am typing way after midnight with the "candlelight" setting just because I can. The tricky part was the dilemma that came afterwards.

In order to work properly, the lights remain "on" all the time (to receive commands) and you turn them on/off via software. So what happens if I enter a room without my phone? At this moment I have three choices: * I can go back, pick my phone, unlock it, turn on the light, and then turn it off once I leave * I can turn the light off and then on again, turning the lights on. I then need to leave them on and turn them off via software once I have my phone back. * I can stay in the dark and convince myself that this is fine.

The solution to this problem is a small remote control I can keep on me all the time and turn lights on and off with as many programmable buttons as smart lights. I know this is a good solution because every single compatible remote is currently sold out.


Even before lights became an issue, my dream was to control my heating to turn it on when I get up and turn it off shortly after. My first step was to buy a cheap Bosch thermostat. This was a bad choice, as Bosch equipment require a central hub with constant internet connection. If you want to turn off your heating you have to send your request to Bosch headquarters, who in turn will tell your hub to tell your heating that it should turn off. Good thing I only bought one.

My second attempt was an Eurotronic Spirit Z-Wave plus. These devices connect via Z-Wave, a protocol that requires a dongle instead of internet connection. My research suggested an Aeotec Z-Stick, which would have been a fine choice if I had stuck to v5. Instead, I bought the newest v7 (newer is always better, right?) which has known compatibility issues and whose firmware can only be upgraded using Windows (a luxury I no longer have). So I bought a v5 and all those problems went away. Both the v5 stick and the Bosch thermostat will be landing on eBay eventually.


All of my equipment was supposed to be controlled with OpenHab, an experience that so far has been mixed: when it works it works great, but when it doesn't it is quite difficult to find out why. That compatibility problem I mentioned above regarding Z-Wave? All I found in the forums (before giving another stick a try) was the suggestion that my thermostat was asleep and that all I had to do was sit next to it and keep it awake for an indeterminate time until it worked. Once I changed the stick, however, everything worked just fine.

I installed OpenHab in my old laptop, where things worked mostly fine. But once I decided to move it to its own Raspberry Pi (using the OpenHabian distro) it all fell into pieces: my Pi is too underpowered, the memory card too slow, and the default Java installer doesn't support my architecture. So not only do I need a new dongle, but also a new Raspberry Pi.

Progress and next steps

The next steps are getting a proper Raspberry Pi, a remote control, and make everything talk to everything else. I still have some hopes for OpenHab - the thermostat have eventually been detected, the lights worked just fine, and the new old Z-Wave dongle does its job. I could look deeper into its voice recognition functions, but since my plan was installing Mycroft I might as well stick to that.

With the working up I have finally been able to re-plug my TV. The TV is old-ish and therefore controlled with a Raspberry Pi 3 running OSMC, with all of its content coming either from YouTube or the NAS (which I have once again turned on). This is not technically part of the smart home, but it is worth mentioning anyway. And no, I am not re-purposing this Raspberry Pi for OpenHab.

And last but not least, a word of caution: living in a non-functioning house (well, apartment) is exhausting, and I do not recommend following my steps until you know things will work fine.

I'll keep you updated.

How my brother's iCloud account was stolen

My brother got his iPhone stolen at gunpoint. This is the story of how he lost control of his iCloud account first (along with years of priceless memories, including my nephew's first steps) and how this couldn't have happened without Apple's incompetent support.

But before that, a plea for help: if you know of anyone who can help us get the account back (or rather, the priceless photos locked inside) please get in touch with the links at the end of the article.

Part 1: Locked out of iCloud

The first thing the thieves did (after running away, of course) was changing the phone number associated with the iCloud account. I do not know how they did this - it has been suggested that Apple will send a code to your phone, which the thieves obviously had. In any case, as soon as my brother tried to log into "Find my Phone" he was faced with a screen asking him to verify the phone number associated with the account, which was set to a number we do not recognize. It didn't matter that we still had the proper password for the iCloud account, nor that we still have control of the e-mail associated with the account. As far as Apple is concerned, if you don't know the phone number (which, again, the thieves changed) you cannot access your iCloud account. This is a known issue with iCloud security.

Next we got in touch with Apple, both via phone and Twitter. Phone support was useless, as all they would say was that they couldn't help us unless we knew the phone number. The closest I got to a victory was getting the phone representative to say out loud that she wanted me to provide the phone number the thieves gave, but that's about it. Even if we have the old phone number, the iCloud e-mail, the iCloud password, a police report and multiple IDs, Apple will not budge. Twitter support was even worse: after repeatedly asking them to read what I said two messages ago, I ended up getting this pointless, infuriating response:

We completely understand the concern with how important it is to have this resolved as soon as possible, and we were able to locate the cases from when you had previously reached out. Based on the information that you've provided, and the steps you and your brother have gone through, if your brother is unable to regain access to his Apple ID we would be unable to provide any additional methods to regain access to the account, and we would be unable to change the trusted phone number on the account. If you have any other questions or concerns regarding this issue, the best option would be to reach back out to our Apple ID experts at the link provided in our previous message (Note: that's the phone we called before).

Part 2: Losing iCloud for good

But the story gets even worse (better?). While we were stuck in phone support hell, my brother got a phishing SMS. He didn't recognize it as such, and lost the phone for good. The trick works like this: once you get a new SIM card (which the thieves can tell because the old one stops working) the thieves send you a phishing e-mail pretending to be from Apple. You follow the link, give your iCloud username and password, and now the thieves can unlock your iPhone and resell it. Crucially, this step only works because thieves know that Apple support will not help you: if Apple had been of ANY help then we would have recovered access long before the SMS and my brother wouldn't have followed the link.

According to Gizmodo (in Spanish) the next step would have been a phishing call with spoofed caller ID. But we will never know.

Sidenote: I reported both URLs (https://apple.iforgot-ip.info and https://apple.located-maps.info) to their hosting providers. Results have been mixed. PublicDomainRegistry.com (which belongs to Webhostbox) will not take down the hosting addresses unless they can see the phishing attack themselves (they won't check logs), but good luck getting a one-time link to work twice. UnifiedLayer.com was helpful, and I believe that GoDaddy revoked their domains.

Part 3: No more Apple

The iCloud website promises "all your stuff — photos, files, notes, and more — is safe, up to date, and available wherever you are". You have now seen what "safe" means in this context: it will be in the cloud, yes, but that doesn't mean you'll have access to it.

The question now is: would my brother choose Apple again? An iPhone is not cheap in general, and in Argentina less so. The current price for an iPhone 13 is ca. 400.000 ARS, which roughly translates to 2200 USD or 1300 USD at the unofficial rate (it's complicated). With an average monthly salary of 427 USD (according to Numbeo) you can see that getting a new iPhone is not a choice to take lightly.

What will my brother do? Paraphrasing from a conversation we had: "if I could get my account back I could consider getting a new iPhone. But if I have to start from scratch then it doesn't make sense. If I can't get my data back I'll probably get an Android phone instead".

Part 4: Conclusion and next steps

I have not entirely given up, but I'm not keeping my hopes up either. We are currently looking into whether my brother's wife can get access to his files (they had some kind of shared access), whether his iWatch can be of any use (it was logged into the iCloud account), whether small claims court is likely to help (I know it would work in the EU but Argentina is trickier), and whether anyone in my extended network can reach someone at Apple who is not an AI (thanks for nothing, LinkedIn).

As for next steps, I will be gifting my family access to some cloud storage, but unless I can get a service with competent tech support I may end up setting up a cloud of my own. Hopefully the loss of my nephew's first steps will not be in vain.

Do you have any ideas? Do you work for Apple? Then send me an e-mail or get in touch in Mastodon or even Twitter.

A bag for life

About 8 years ago I was searching for a new messenger bag - the one I had was good but too small, its only one big pocket was inconvenient, and the zipper was starting to fail. And since I was planning on bringing this bag with me everywhere, I needed a bag that could survive being dragged on a classroom floor, exposure to sun and rain, and fit under an airplane seat. Anything requiring "gentle care" was immediately out. Backpacks were also ruled out for a simple reason: in my country of origin backpacks are often cut open to steal their contents, so I wanted a bag I could always keep my eyes on.

My first purchase was a Timbuk2 bag which I bought on Amazon because there were no local retailers (looks exactly like this). Their marketing at the time promised "the last bag you'll ever buy" (or something to that effect), which was exactly what I wanted. But you know what the problem with lifetime guarantees is? That you can only judge them with customers who have used them for a lifetime, which disqualifies essentially everything made after at least the 2000s. Luckily (for you) you don't have to wait that long because a year or two after buying my bag the waterproofing started peeling off (along with a bit of the interior). As for the "lifetime guarantee", it turns out it is only valid for customers who bought their bag in an official shop. I got mine from Amazon, so no servicing for me. It has now been relegated to ugly sports bag status.

My second bag is an Aunts & Uncle's leather bag that I bought 7 years ago. The actual model is no longer for sale, but this one is very similar. I chose leather because, unlike more modern materials, I knew this would last forever (we still have bags from the 1800s around after all). And to be fair the bag's exterior is fine - it's a bit beaten up, but that's just part of its charm. The real problem is that the bag doesn't close anymore: the closing system uses Velcro (sorry, "hook and loop") which always made the bag impossibly loud to open. It was loud in daily life, it was loud in a quiet classroom, and it was definitely loud on a train at night. Eventually the material gave up and stopped closing altogether, which made it much quieter (good) at the cost of risking everything falling out (bad). I got in touch with the manufacturer after reading their sustainability compromise to get some proper belts installed, but the only solution they offered was to replace the Velcro and make it loud again.

I'm currently looking into leather working to see whether I could install some belts myself without making a mess. I would love to pay someone to do it, but apparently my city has exactly one woman who could do the job and she's on a sabbatical. I'll also probably peel all of the remaining waterproofing flakes of my other bag, spray it with some generic waterproofing, and make it look at least decent.

What would I recommend to someone who wants "a bag for life"? My suggestion would be to go for something simple that one can fix by themselves: while I currently don't have the leather working skills for doing the job, at least there's a clear path forward for my second bag. But sourcing, cutting and reapplying plastic liner for the first one? Good luck with that.

Further reading: some interesting comments regarding this article on the quality of modern home appliances.

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