This model is a fine-tuned version of Google's MuRIL (Multilingual Representations for Indian Languages) BERT architecture, specifically adapted for multi-class Cyberbullying & Hate Speech Detection. It categorizes social media text and online commentary across 6 categories: 1. age: Cyberbullying targeting an individual's age. 2. ethnicity: Bullying or hate speech targeting ethnicity, race, or caste. 3. gender: Misogyny, sexism, or gender-based harassment. 4. religion: Hate speech or insults targeting religious beliefs. 5. othercyberbullying: General toxic harassment, insults, or threats. 6. notcyberbullying: Benign, safe, neutral, or positive text. Evaluated on the held-out multi-class…
Open-weight model · Text classification
SciFive-base-Pubmed_PMC
by Razent razent/SciFive-base-Pubmed_PMC
SciFive-base-Pubmed_PMC is an open-weight model for text classification from Razent. It has 223M parameters. At 16-bit it needs about 0.5 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 4.6k downloads a month.
Paper: SciFive: a text-to-text transformer model for biomedical literature Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet For more details, do check out our Github repo.
Parameters223M
Context—
Weights3.6 GB
License—
AccessOpen weights
Monthly Downloads4.6k
Runs On
What it takes to serve SciFive-base-Pubmed_PMC (223M 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 | 0.4 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.2 GB | 0.3 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 Oct 16, 2026.
SciFive-base-Pubmed_PMC on every accelerator the SAVRN Index prices, at every precision
Model Card
Paper: SciFive: a text-to-text transformer model for biomedical literature Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet For more details, do check out our Github repo.
Excerpt from the card by Razent.
Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 32,128
- Model type
- t5
Identity and Version
- Repository
- razent/SciFive-base-Pubmed_PMC
- Publisher
- Razent
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 223M parameters
- Languages
- en
- Revision
- fceb43a5ee11569bfe906b65098d78048b3403d8
- First published
- 2022-03-02
- Last updated
- 2023-08-30
Files and Weights
9 files, 3.6 GB in total. The weights are 4 files totalling 3.6 GB in bin, h5, msgpack, safetensors.
Weights4 files · 3.6 GB
Configuration1 file · 581 B
Tokenizer2 files · 2.2 MB
Documentation1 file · 1.4 KB
Repository1 file · 836 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| flax_model.msgpack | Weights | 891.6 MB | f9057f257b3e |
| model.safetensors | Weights | 891.6 MB | 019df4a542d8 |
| pytorch_model.bin | Weights | 891.7 MB | 8a4d00b21806 |
| tf_model.h5 | Weights | 892.1 MB | df11afa7cf51 |
| config.json | Configuration | 581 B | — |
| README.md | Documentation | 1.4 KB | — |
| .gitattributes | Repository | 836 B | — |
| spiece.model | Tokenizer | 791.7 KB | d60acb128cf7 |
| tokenizer.json | Tokenizer | 1.4 MB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 3.6 GB
Download from Razent
Released by Razent through its official repository on Hugging Face.
Built From
- Described by arXiv:2106.03598
- Trained on (disclosed) pmc/open_access
- Trained on (disclosed) pubmed
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 3.6 GB |
| 16-bit | 0.4 GB |
| 8-bit | 0.2 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About SciFive-base-Pubmed_PMC
How much GPU memory does SciFive-base-Pubmed_PMC need?
About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (223M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run SciFive-base-Pubmed_PMC 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.
Similar Models
Model · Text classification
DeBERTa-v3-base-mnli-fever-docnli-ling-2c
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: MultiNLI, Fever-NLI, LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation). It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". The DocNLI merges the classes "neural" and "contradiction" into "not-entailment" to enable the inclusion of the DocNLI dataset. The base model is DeBERTa-v3-base from Microsoft. The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different…
This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs. Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions. This model classifies inputs into benign (0) and injection-detected (1). deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English. It does not detect jailbreak attacks…
DEBATE-kor-base is a Korean-adapted Political DEBATE model for binary natural language inference (NLI) on political text. The model is initialized from mlburnham/PoliticalDEBATEDeBERTabasev1.1, the original DeBERTa-based Political DEBATE checkpoint, and subsequently fine-tuned on jongrock17/PolNLI-kor, a Korean translation and adaptation of PolNLI. Political DEBATE DeBERTa-base → PolNLI-kor → DEBATE-kor-base Unlike the PolNLI-kor-RoBERTa model family, which starts from Korean-pretrained KLUE-RoBERTa encoders, DEBATE-kor directly adapts the original Political DEBATE checkpoint to Korean political NLI. DEBATE-kor formulates NLI as a binary classification problem. notentailment combines…
This is a three-class Japanese/English sentiment classifier for short hospitality reviews, fine-tuned from google-bert/bert-base-multilingual-cased. It categorizes each review as negative, neutral, or positive. The model predicts one overall label for a short Japanese or English hospitality It is intended for research, prototyping, and human-reviewed analytics. It must not be used as the sole basis for consequential business, employment, moderation, or customer-service decisions. Applications using this model should allow operators to review and correct its predictions. The model was fine-tuned on 480 synthetic bilingual reviews: 240 Japanese and 240 English, balanced across the three…
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).