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Open-weight model · Text classification
inclusively-classification
by E-MIMIC E-MIMIC/inclusively-classification
This model is an Italian classification model fine-tuned from the Italian BERT model for the classification of inclusive language in Italian. It has been trained to detect three classes: - inclusive: the sentence is inclusive (e.g.
Parameters111M
Context512
Weights885.7 MB
Licensecc-by-nc-sa-4.0
AccessOpen weights
Monthly Downloads552.2k
Runs On
What it takes to serve inclusively-classification (111M 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.
Model Card
This model is an Italian classification model fine-tuned from the Italian BERT model for the classification of inclusive language in Italian. It has been trained to detect three classes: - inclusive: the sentence is inclusive (e.g. "Il personale docente e non docente") - notinclusive: the sentence is not inclusive (e.g. "I professori") - notpertinent: the sentence is not pertinent to the task (e.g. "La scuola è chiusa") The model has been trained on a dataset containing: - 8580 training sentences - 1073 validation sentences - 1072 test sentences The data collection has been manually annotated by experts in the field of inclusive language (dataset is not publicly available yet). The model…
Excerpt from the card by E-MIMIC, licensed cc-by-nc-sa-4.0.
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 32,102
- Stored precision
- float32
- Model type
- bert
Identity and Version
- Repository
- E-MIMIC/inclusively-classification
- Publisher
- E-MIMIC
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 111M parameters
- Languages
- Not stated by the source
- Revision
- 6875dbf74b712cc7bdfa4a4d387baa064e5c32cb
- First published
- 2023-06-23
- Last updated
- 2025-06-25
Files and Weights
8 files, 885.9 MB in total. The weights are 3 files totalling 885.7 MB in bin, safetensors.
Weights3 files · 885.7 MB
Configuration1 file · 890 B
Tokenizer2 files · 235.2 KB
Documentation1 file · 3.0 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 442.8 MB | 624a5d71c4ea |
| pytorch_model.bin | Weights | 442.9 MB | f11f5dfedbc3 |
| training_args.bin | Weights | 3.0 KB | bc383b5651db |
| config.json | Configuration | 890 B | — |
| README.md | Documentation | 3.0 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer_config.json | Tokenizer | 59 B | — |
| vocab.txt | Tokenizer | 235.1 KB | — |
License and Download
- License
- cc-by-nc-sa-4.0
- Access
- Open weights, no gate
- Download size
- 885.7 MB
Download from E-MIMIC
Released by E-MIMIC through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 885.7 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 inclusively-classification
How much GPU memory does inclusively-classification need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (111M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run inclusively-classification 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 inclusively-classification commercially?
Not without separate permission. inclusively-classification is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. CC BY-NC-SA 4.0 permits non-commercial sharing and adapting with credit, and requires adaptations to use the same license. Commercial use needs separate permission.
What is inclusively-classification's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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