For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689 CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.) The model was trained on the following labels: "Bearish": 0, "Neutral": 1, "Bullish": 2 CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from…
Open-weight model · Text classification
roberta-base-go_emotions
by Sam Lowe SamLowe/roberta-base-go_emotions
Model trained from roberta-base on the goemotions dataset for multi-label classification. A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-goemotions-onnx.
Parameters125M
Context514
Weights997.4 MB
Licensemit
AccessOpen weights
Monthly Downloads636.4k
Runs On
What it takes to serve roberta-base-go_emotions (125M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
SAVRN's Notes on roberta-base-go_emotions
Tagging short text with emotion labels, several per passage, is the job Sam Lowe fine-tuned this 125M-parameter RoBERTa classifier for, using the go_emotions dataset. At 16-bit it loads 0.2 GB of weights into 0.3 GB of memory, so any GPU on the Index is oversized for a single copy; the cheapest, one MI300X with 192 GB at $1.85 an hour, is really priced for whatever larger model produces the text you are scoring, with this riding alongside. Stored precision is float32 and the download is 1.0 GB across 12 files.
MIT is about as light as licensing gets: commercial use, modification and redistribution, provided the copyright and permission notices travel with the model. Check the 514-token context, which means long documents get split, and note the publisher also offers an ONNX build with an INT8 variant that cuts file size by 75%, the better fit for small-batch inference.
Model Card
By Sam Lowe, published under mit, revision d75048347613.
Overview
Model trained from roberta-base on the go_emotions dataset for multi-label classification.
ONNX version also available
A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx. These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference.
Dataset used for the model
go_emotions is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label.
How the model was created
Configuration
- Architecture
- RobertaForSequenceClassification
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,265
- Stored precision
- float32
- Model type
- roberta
Identity and Version
- Repository
- SamLowe/roberta-base-go_emotions
- Publisher
- Sam Lowe
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 125M parameters
- Languages
- en
- Revision
- d75048347613a25d77de8cf6412eaae9fa7b26be
- First published
- 2022-09-15
- Last updated
- 2026-05-13
Files and Weights
12 files, 1.0 GB in total. The weights are 2 files totalling 997.4 MB in bin, safetensors.
Weights2 files · 997.4 MB
Configuration3 files · 7.5 KB
Tokenizer4 files · 3.4 MB
Documentation2 files · 10.6 KB
Repository1 file · 1.4 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 498.7 MB | 84d6d338b4cf |
| pytorch_model.bin | Weights | 498.7 MB | 4fd088956d38 |
| config.json | Configuration | 1.9 KB | — |
| special_tokens_map.json | Configuration | 280 B | — |
| trainer_state.json | Configuration | 5.3 KB | — |
| LICENSE | Documentation | 1.1 KB | — |
| README.md | Documentation | 9.6 KB | — |
| .gitattributes | Repository | 1.4 KB | — |
| merges.txt | Tokenizer | 456.4 KB | — |
| tokenizer.json | Tokenizer | 2.1 MB | — |
| tokenizer_config.json | Tokenizer | 380 B | — |
| vocab.json | Tokenizer | 798.3 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 997.4 MB
Download from Sam Lowe
Released by Sam Lowe through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) go_emotions
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 997.4 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About roberta-base-go_emotions
How much GPU memory does roberta-base-go_emotions need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (125M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run roberta-base-go_emotions on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use roberta-base-go_emotions commercially?
Yes. roberta-base-go_emotions is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is roberta-base-go_emotions's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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