isomer007/takemeter-distilbert is a 3-class text classifier fine-tuned from distilbert-base-uncased on 153 hand-labeled r/CryptoCurrency posts. It sorts crypto-community posts by the quality of the take — not by whether a post is bullish or bearish, but by whether its claim is backed by load-bearing, checkable evidence.
The fine-tuned model is the center of the AlphaSift project, which also includes a zero-shot llama-3.3-70b-versatile baseline and a Gradio demo. The complete writeup — taxonomy, label definitions, error analysis, and a side-by-side comparison with the baseline — lives in the project README.
The headline finding from the project evaluation is that this fine-tuned model loses to a zero-shot llama-3.3-70b-versatile baseline on the locked 23-post test set (0.522 vs 0.783 accuracy, a 0.261 gap). The cause is a data-quantity failure on 107 training examples, not a pipeline bug — see the Evaluation section below. The model is published because (a) the failure mode is itself a useful finding, and (b) classifier.py and app.py in the repo load it cleanly so the result is reproducible.
The model's intended use is classifying a single short Reddit-style post (≤256 tokens) as one of three labels:
Label
Definition
signal
Claim backed by load-bearing, verifiable evidence — on-chain data, a named mechanism, a concrete historical comparison. Direction (bullish or bearish) does not matter.
hype
Bullish claim with no real evidence — bare price targets, FOMO, "to the moon."
panic
Bearish claim with no real evidence — doom, "scam," nothing behind it. (The community's native term for this is "FUD.")
It is a research / educational artifact that demonstrates the failure mode of fine-tuning a small encoder on a tiny hand-labeled dataset, and the value of comparing against a strong zero-shot baseline.
Downstream Use
Component in a larger crypto-content pipeline. A downstream user could treat the model's signal probability as a "quality score" attached to each post and aggregate it across a feed.
Comparison baseline. Anyone fine-tuning a small text classifier on a domain-specific taxonomy can compare against this model to see what 107 training examples buys you (and what it doesn't).
Discussion-quality moderation signal. Could be used as one input among several for surfacing "reasoned" vs. "emotional" posts in a community feed — but not as a single decision-maker, for the reasons in the next section.
Out-of-Scope Use
Financial advice or trading signal generation. The model classifies discourse quality, not market direction. It does not predict prices, sentiment polarity, or trade outcomes. Any system that wires this model's output directly into a trading decision is misusing it.
Posts outside the r/CryptoCurrency distribution. Trained on a single subreddit's idiom (informal, US-English, crypto-native vocabulary, lots of slang like "FUD", "hopium", "rug", "LFG", "GM", emoji-heavy). Performance will degrade on more formal financial writing, non-English text, or posts about adjacent domains (general finance, NFTs without the trading angle, pure tech discussion of blockchain protocols).
Posts with no stance. The 3-class taxonomy is gated: a post with no take at all (news links, neutral questions, memes) is supposed to be filtered out upstream and is not one of the model's three classes. Routing filter-like content to this model will produce a forced 3-way prediction that should be ignored.
High-stakes content moderation. A 0.522 accuracy on a 23-post test set is below random-oracle quality for any moderation decision with real consequences.
Inference on text longer than 256 tokens. Posts are truncated to max_length=256 at inference time; longer posts will have their tail silently cut off.
Bias, Risks, and Limitations
Known limitations of this model
Below the zero-shot baseline on the project's locked test set. 0.522 vs 0.783 accuracy for llama-3.3-70b-versatile zero-shot on the same 23 posts. The fine-tuned model is not the recommended classifier in the AlphaSift project; the baseline is.
Confidence is at chance level on every prediction. In the reported evaluation run, every prediction (right or wrong) had a softmax confidence in the 0.34–0.35 band, essentially indistinguishable from the 0.333 a uniform 3-way guess would produce. The model is not confidently right or confidently wrong; it sits near the decision boundary between all three classes. Do not treat the confidence number as a reliable signal.
Instability across training runs. Re-running the same training notebook on the same data with the same seed produced a different model with a different "broken" class. The model's behavior is not a stable function of the training pipeline.
Data-quantity failure. 107 training examples spread across 3 classes (as few as 24 for hype) is well below the threshold at which a 6-layer transformer can pin down a reproducible decision boundary. The model has learned weak proxies — see the Reflection section in the project README — not the intended rule.
Small test set. All reported numbers are on 23 posts; the smallest per-class support is 5 (hype), so a single flipped prediction moves that class's F1 by ~0.10–0.15. Per-class numbers should be read as directional, not precise.
Sentiment proxies. The model partially recovers a sentiment axis (positive ↔ negative language) it was never asked to learn, and uses it as a shortcut. The "FTX/Anthropic stake breakdown" example in the project README — rigorous bearish-evidence content — is misread as panic because of negative-coded vocabulary.
Numeric-density proxy. The model uses "contains numbers" as a shortcut for signal. The "buying at 60k, betting on 200k" post — dense with price targets but no mechanism — is misread as signal instead of hype. This is the one error pattern that recurred identically across both training runs.
Known biases of the training data
Single-subreddit source. All 206 labeled rows are r/CryptoCurrency posts. Crypto-Twitter, crypto-Discord, and other subreddits will have different discourse norms.
Hand-pick survivorship bias. The 250 candidates handed to the labeling pipeline were pre-selected by browsing the sub; stanceless content was already under-collected upstream of the labeling pass. The dataset is clean and taxonomy-consistent, not a representative sample.
Pre-label anchoring. An LLM (Claude) pre-labeled candidates before hand-review; 16/206 labels (7.8%) were corrected on review, but the prelabel may have anchored the reviewer on the rest. A v2 protocol would blind the reviewer to the prelabel on a sample to measure anchoring directly.
Sentiment imbalance in signal. The dataset has more bullish-evidence posts than bearish-evidence posts, both because bullish-evidence content is more common on r/CryptoCurrency and because rigorous bearish analysis is harder to find. This contributes to the signal → panic error pattern.
Length imbalance. The training set is dominated by longer, citation-heavy posts in each class. Short, terse signal posts (e.g. "It is actually more profitable now to rent out GPU time to AI than it is to mine w/ ASICs") are underrepresented and frequently misread.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More specific recommendations:
Do not deploy this model as a standalone classifier. Use the llama-3.3-70b-versatile zero-shot baseline from the same project, which outperforms it on the project's own test set.
If you must use this model, treat its output as a weak signal and the confidence number as uninformative (it sits at chance). Apply it only on posts that are already known to express a stance (filter has been removed upstream), and only on short Reddit-style English text.
For any decision with consequences, re-evaluate on a larger, balanced test set, or fall back to the zero-shot baseline.
A v2 of this model would need (a) ≥10× the training data per class, (b) targeted counter-examples to break the sentiment- and density-proxy heuristics (short terse signal posts, number-dense hype posts, balanced-sentiment signal posts), and (c) bootstrap confidence intervals on every reported metric, not point estimates on n=23.
How to Get Started with the Model
Use the code below to get started with the model. The transformers pipeline is the lightest path; the project's own classifier.py wraps the same call with the AlphaSift label order and a 256-token truncation.
python
1from transformers import pipeline
23classifier = pipeline(4"text-classification",5 model="isomer007/takemeter-distilbert",6 top_k=None,# return probabilities for all 3 classes7 truncation=True,8 max_length=256,9)1011result = classifier("9 UMA whale wallets control 53.1% of voting power. "12"Same wallets fund $12.5M Polymarket side-bets on "13"markets they resolve. The oracle is mathematically "14"incentivized to lie.")15# Returns: [[{'label': 'signal', 'score': ~0.34},16# {'label': 'panic', 'score': ~0.33},17# {'label': 'hype', 'score': ~0.33}]]18# Note: the project evaluates this exact post as correctly labeled 'signal',19# but the model's confidence is at chance — see the Limitations section.2021# Or, for a single top label and confidence:22top = classifier("SOL is so back. This is the floor, screenshot this. LFG 🚀🚀")23print(top[0][0])# {'label': 'hype', 'score': ~0.34}
For the full project — including the Groq baseline, evaluation, and Gradio demo — clone the repo and use the included classifier.py and app.py:
153 labeled examples used for training (after dropping filter rows and stratified-splitting 70/15/15), drawn from a hand-collected pool of ~250 r/CryptoCurrency posts.
206 total rows in the full labeled dataset, with a filter rate of 25.7% (53/206). The remaining 153 are split:
signal: 65 rows (42.5% of usable)
panic: 54 rows (35.3% of usable)
hype: 34 rows (22.2% of usable)
No class exceeds 70% of the usable set; every quality class is at or above the 20% floor.
The full dataset is committed to the project repo at data/alphasift_dataset.csv (with [REVIEWED] flags in the notes column showing which rows were hand-corrected from the AI prelabel).
The label taxonomy (definitions, decision tree, edge-case rules) is at data/taxonomy.md in the project repo.
Licensing: The posts are public Reddit comments; they inherit Reddit's user-content license. They are not redistributed as a standalone dataset; they are part of the AlphaSift project repo.
Labeling process
AI-assisted cleaning pass. Claude filtered out stanceless content (news links, questions, memes, sponsored posts) from the 250 hand-collected candidates. Kept posts were re-read to confirm nothing load-bearing was dropped.
AI pre-labeling. Claude pre-labeled the remaining candidates (flagged as prelabeled in the notes column).
Hand-review pass. Every one of the 206 final rows was then read and hand-reviewed against the taxonomy's decision rule. 16 of 206 rows (7.8%) had their label corrected on review; all 206 rows are now marked [REVIEWED] in notes, with the original prelabel preserved in the note text where it was overridden along with the reasoning for the change.
Head: 3-class sequence-classification head; id2label = {0: "hype", 1: "panic", 2: "signal"}, taken from config.py so the head's label order matches the rest of the repo.
Preprocessing: Posts are tokenized with the DistilBERT uncased tokenizer, truncated to max_length=256, and padded. filter rows are dropped before training, so the model only ever sees signal / hype / panic.
Trainer: HuggingFace Trainer with the following hyperparameters:
Data split: stratified 70/15/15 on the 153 usable rows → 107 train / 23 validation / 23 test. The split is seeded (random_state=42) so the test set is reproducible locally via evaluate.py.
Training Hyperparameters
Training regime: fp32 (full precision; no mixed precision was used). The model is small enough (~67M parameters) that fp32 fits comfortably on a free Colab GPU.
Speeds, Sizes, Times
Training time: single-digit minutes per run on a free Colab GPU (T4); the notebook ran end-to-end in well under the 90-minute Colab free-tier cutoff.
Checkpoint size: ~255 MB (the standard DistilBERT float32 size on disk).
Inference latency: low single-digit milliseconds per post on a modern CPU; the Gradio demo runs comfortably on the HF Spaces free CPU tier.
Evaluation
All numbers below are on the same locked 23-post test set (the 15% test split from the stratified split above). The full per-class breakdown, confusion matrix, and per-post error analysis are in the project README's Evaluation report section.
Testing Data, Factors & Metrics
Testing Data
23 posts held out from the same hand-labeled r/CryptoCurrency pool used for training, stratified across the three quality classes:
signal: 10 posts
panic: 8 posts
hype: 5 posts
Tokenized with the same DistilBERT uncased tokenizer at max_length=256.
Labeled by the same hand-review process as the training data (see Training Data above).
The full test set is reproducible locally: python evaluate.py regenerates the exact 23 posts from the locked seed and re-runs both models.
Factors
The evaluation is disaggregated by:
True class (signal / panic / hype) — reported as per-class precision, recall, F1, and support, plus a confusion matrix.
Sentiment direction of the post (bullish vs. bearish) — relevant because the dominant error is signal → panic for bearish-evidence posts (the model uses negative-sounding vocabulary as a proxy for panic).
Post length and numeric density — relevant because the model uses length and numeric density as proxies for signal.
The evaluation is not disaggregated by subreddit, language, or time period (all 23 posts are r/CryptoCurrency, English, contemporaneous with the training data).
Metrics
Accuracy — overall fraction of correct top-1 predictions.
Per-class precision, recall, F1, support — standard classification_report output.
Macro and weighted F1 — to summarize across the imbalanced class distribution.
Confusion matrix — to make the dominant error pattern (signal → panic, 6 of 10 true signal posts) visible at a glance.
Confidence calibration — the softmax probability of the predicted class on every test post, reported in the project README. All predictions in the reported run fall in the 0.34–0.35 band.
Zero-shot baseline (llama-3.3-70b-versatile via Groq, no fine-tuning)
For context — the baseline that beat this model on the same test set:
Class
Precision
Recall
F1
Support
hype
0.67
0.80
0.73
5
panic
0.78
0.88
0.82
8
signal
0.88
0.70
0.78
10
macro avg
0.77
0.79
0.78
23
weighted avg
0.80
0.78
0.78
23
Overall accuracy: 0.783
The zero-shot baseline wins on every class: hype 0.73 vs 0.50 F1, panic 0.82 vs 0.55 F1, signal 0.78 vs 0.50 F1. It never saw a single training example and still outperforms this model, because it brings pretrained world knowledge of what each kind of take sounds like — knowledge that 107 examples can't replace.
Summary
Model
Test accuracy
Zero-shot baseline (llama-3.3-70b-versatile)
0.783
Fine-tuned DistilBERT (this model)
0.522
Difference
−0.261 (this model regresses vs. baseline)
The dominant error pattern is signal → panic (6 of 10 true signal posts). The model is not learning the intended evidence-quality rule; it is learning two cheaper proxies — negative-sounding words ⇒ panic and numeric density ⇒ signal/non-panic. The full per-post error analysis is in the project README's "Wrong predictions, analyzed" section.
The model also exhibits run-to-run instability: re-running the same training notebook on the same data with the same seed produced a different model where hype collapsed to 0% recall instead of signal. The specific failure mode is not stable; the underlying problem (data quantity) is.
Model Examination
The model's learned behavior was examined qualitatively via per-post error analysis on the locked test set, not via internal interpretability work (attention probing, probing classifiers, etc.). Three findings:
Sentiment proxy. The model treats negative-coded vocabulary as evidence of panic, even when the post is rigorously evidence-backed (the FTX/Anthropic stake breakdown is the clearest example). This is consistent with the model having recovered a sentiment axis it was never asked to learn.
Numeric-density proxy. The model treats the presence of numbers as evidence of signal, even when the numbers are bare price targets with no mechanism (the "buying at 60k, betting on 200k" post). This proxy is the one error pattern that recurred identically across both training runs.
Chance-level confidence on every prediction. Every prediction in the reported run has a softmax confidence in the 0.34–0.35 band — indistinguishable from uniform 3-way chance. The model is near the decision boundary between all three classes on essentially every input, which is consistent with a model that has not pinned down a stable decision boundary at all (data-quantity failure, not mislearned-rule failure).
No formal probing, attention visualization, or embedding-space analysis has been performed; these would be reasonable follow-ups but are out of scope for the v1.
Hours used: single-digit minutes per training run; the model was fine-tuned only a small number of times during development
Cloud Provider: Google Cloud (via Colab)
Compute Region: US (Colab default)
Carbon Emitted:Negligible for this scale of fine-tuning. A single Colab T4 run of a 67M-parameter model for a few minutes is in the low single-digit grams of CO₂eq range, well under the threshold worth reporting precisely. The bigger environmental cost in the project is the zero-shot baseline (the llama-3.3-70b-versatile calls via Groq), which is the recommended classifier and is not done with this model at all.
Technical Specifications
Model Architecture and Objective
Architecture:DistilBertForSequenceClassification — a 6-layer DistilBERT encoder with a 3-class linear classification head on top of the [CLS] pooled output. seq_classif_dropout=0.2 on the pooled output before the head.
Objective: Cross-entropy loss on a 3-way single-label classification (problem_type: "single_label_classification").
Input: A single r/CryptoCurrency-style post, tokenized with the DistilBERT uncased tokenizer, truncated to 256 tokens, padded to the longest in-batch sequence.
Output: A 3-way softmax over {hype, panic, signal}. The head's id2label is {0: "hype", 1: "panic", 2: "signal"} and label2id is {hype: 0, panic: 1, signal: 2}. Note: the project repo's config.pyLABEL_MAP matches this order exactly; classifier.py verifies the loaded model's id2label matches config.ID_TO_LABEL at load time and raises if not.
Training: Google Colab free tier (NVIDIA T4, 16 GB VRAM)
Inference: CPU is sufficient (the Gradio demo runs on the HF Spaces free CPU tier)
Software
Python: 3.10+
PyTorch: 2.x
Transformers: 4.x / 5.x (model was pushed with transformers_version: 5.10.2 in config.json)
Other dependencies:gradio, scikit-learn, pandas, numpy — see requirements.txt in the project repo
Citation
If you use this model, please cite the AlphaSift project:
APA:
isomer007. (2026). AlphaSift: A fine-tuned DistilBERT text classifier for
crypto-community take-quality. Hugging Face Model ID: isomer007/takemeter-distilbert.
https://huggingface.co/isomer007/takemeter-distilbert
BibTeX:
bibtex
1@misc{alphasift2026takemeter,
2 author = {isomer007},
3 title = {AlphaSift: a fine-tuned DistilBERT text classifier for
4 crypto-community take-quality},
5 year = {2026},
6 howpublished = {\url{https://huggingface.co/isomer007/takemeter-distilbert}},
7 note = {Model card. Fine-tuned from \texttt{distilbert-base-uncased}
8 on 153 hand-labeled r/CryptoCurrency posts. Evaluated against
9 a zero-shot \texttt{llama-3.3-70b-versatile} baseline; the
10 baseline outperforms the fine-tuned model on the project's
11 locked 23-post test set (0.783 vs 0.522 accuracy). Project
12 repository: \url{https://github.com/isomer04/alphasift}.}
13}
Base model citation (DistilBERT):
bibtex
1@inproceedings{sanh2019distilbert,
2 title = {DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
3 author = {Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
4 booktitle = {EMC2 NLP Workshop at NeurIPS 2019},
5 year = {2019}
6}
Glossary
signal — a post whose claim is backed by load-bearing, checkable evidence (on-chain data, a named mechanism, a concrete historical comparison). Direction (bullish or bearish) does not matter.
hype — a bullish post with no real evidence. The community's native term for the equivalent bearish case is "FUD"; this project calls that panic for symmetry.
panic — a bearish post with no real evidence.
filter — a post with no stance at all (news links, neutral questions, memes). Excluded from the 3-way taxonomy. Not a class of this model; the model is trained only on signal / hype / panic.
FUD — community shorthand for "fear, uncertainty, and doubt"; maps to panic in this taxonomy.
Hopium — community shorthand for groundless bullishness; maps to hype.
Zero-shot baseline — the comparison classifier that classifies a post with no task-specific training, using only a system prompt that defines the labels. In this project, the baseline is llama-3.3-70b-versatile via the Groq API.
Locked test set — the 23-post stratified test split, seeded with random_state=42, reproducible locally via python evaluate.py. The "locked" framing is to emphasize that all reported numbers in the project are on the same posts.
Run-to-run instability — the project's finding that re-running the same training notebook on the same data with the same seed produces a different fine-tuned model with a different "broken" class. Symptom of insufficient data per class.
