finance-sentiment-ja-base
bardsai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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finance-sentiment-zh-base
bardsai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗elephant-1
baselabs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mammoth-1
baselabs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-0.6B-finetuned
bd2lcco基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Modern-FinBERT-large
beethogedeon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tradepulse-finbert-sentiment
Bencode92**Task**: Sentiment Classification **Target Column**: `label` **Labels**: ['negative', 'neutral', 'positive'] *Last training: 2026-04-20 17:22* *Dataset: `base_reference.csv` (1797 samples)* - **Base Model**: Bencode92/tradepulse-finbert-sentiment - **Training Mode**: Incremental - **Epochs**: 2 - **Learning Rate**: 1e-05 - **Batch Size**: 4 - **Class Balancing**: None
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View details ↗wtp-bert-mini
benjamin基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wtp-bert-tiny
benjamin基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wtp-canine-s-12l
benjamin基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗klue-roberta-small-3i4k-intent-classification
bespin-global基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Bespoke-MiniCheck-7B
bespokelabs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Bespoke-Nimble-9B
bespokelabs> **Update (September 24, 2026):** Updated to our latest 9B checkpoint, with an 8,192-token context limit and up to 255 choices per field. This release uses T=1.0; the earlier checkpoint remains available at `revision="original-2676"`, and v2 is unchanged. A Qwen3.5-9B LoRA adapter for context-grounded choices, booleans, and rubric score levels. It scores the allowed answer tokens directly; the evaluated workflow does not generate reasoning or free-form answers. This repository contains the adapter (about 165 MiB), tokenizer, exact prompt builder, reference inference code, and release metadata. It requires the Qwen3.5-9B base checkpoint. The full base weights are not duplicated here.
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View details ↗albert-base-v2-emotion
bhadresh-savani* Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗bert-base-go-emotion
bhadresh-savani基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-uncased-emotion
bhadresh-savaniBert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below training parameters ```bibtex @misc{savani2021bertemotion, author = {Savani, Bhadresh}, title = {{bert-base-uncased-emotion: BERT Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion} }
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View details ↗distilbert-base-uncased-emotion
bhadresh-savanil6RfqcHPlI0jWr7TVGoFsWZ64YAg - type: recall value: 0.927 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjEyYmZiZDQ4MzM1ZmQ2ZmJhZWU4OTVkNmViYjA5NzhiN2MxODE0MzUxZTliZTk0MzViZDAyNGU4MDFjYjM1MSIsInZlcnNpb24iOjF9.9lazxLXbPOdwhqoYtIudwRwjfNVZnUu7KvGRklRP_RAoQStAzgmWMIrT3ckX_d5_6bKZH9fIdujUn5Qz-baKBw - type: recall value: 0.927 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWVhMzY0YTA4YmQzYTg4YTBiMzQ5YzRiZWJhMjM1NjUzZGQxZmQ5M2NkZDcyNTQ0ZmJjN2NkY2ZiYjg0OWI0ZCIsInZlcnNpb24iOjF9.QgTv726WCTyvrEct0NM8Zpc3vUnDbIwCor9EH941-zpJtuWr-xpdZzYZFJfILkVA0UUn1y6Jz_ABfkfBeyZTBg - type: f1 value: 0.8825061528287809 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzQzZTJkMDAwOTUwMzY3ZjI2MjIxYjlmZTg3YTdhNTc4ZjYyMmQ2NDQzM2FmYzk3OGEzNjhhMTk3NTQ3OTlhNyIsInZlcnNpb24iOjF9.hSln1KfKm0plK7Qao9vlubFtAl1M7_UYHNM6La9gEZlW_apnU1Mybz03GT2XZORgOVPe9JmgygvZByxQhpsYBw - type: f1 value: 0.927 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzljODQ3NjE3MDRkODE3ZjFlZmY5MjYyOGJlNDQ4YzdlZGRiMTI5OGZiZWM2ODkyZjMyZWQ3MTkzYWU5YThkOCIsInZlcnNpb24iOjF9.7qfBw39fv22jSIJoY71DkOVr9eBB-srhqSi09bCcUC7Huok4O2Z_vB7gO_Rahh9sFgKVu1ZATusjTmOLQr0fBw - type: f1 value: 0.926876082854655 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjJhN2UzODgxOWQ0Y2E3YTcwZTQxMDE0ZWRmYThjOWVhYWQ1YjBhMzk0YWUxNzE2ZjFhNWM5ZmE2ZmI1YTczYSIsInZlcnNpb24iOjF9.nZW0dBdLmh_FgNw6GaITvSJFX-2C_Iku3NanU8Rip7FSiRHozKPAjothdQh9MWQnq158ZZGPPVIjtyIvuTSqCw - type: loss value: 0.17403268814086914 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTVjZmFiOGQwZGY1OTU5YWFkNGZjMTlhOGI4NjE3MGI4ZDhkODcxYmJiYTQ3NWNmMWM0ODUyZDI1MThkYTY3ZSIsInZlcnNpb24iOjF9.OYz5BI3Lz8LgjAqVnD6NcrG3UAG0D3wjKJ7G5298RRGaNpb621ycisG_7UYiWixY7e2RJafkfRiplmkdczIFDQ --- ```bibtex @misc{savani2021distilbertemotion, author = {Savani, Bhadresh}, title = {{distilbert-base-uncased-emotion: DistilBERT Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion} } * Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗roberta-base-emotion
bhadresh-savani```bibtex @misc{savani2021robertaemotion, author = {Savani, Bhadresh}, title = {{roberta-base-emotion: RoBERTa Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/roberta-base-emotion} } * Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗layoutlmv3_document_classification
Bigheadjoshy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗binomial-shannon-2
BinomialTechnologiesA **financial news characterizer with two modes**: it reads *ticker-tagged* company news the way binomial-shannon-1 does (19 structured features), and reads *macro* news (central banks, inflation, rates, FX, commodities, geopolitics) with a dedicated 35-output macro head bank. A built-in router selects the right head set per article. ~15-30 ms on CPU. **Ticker mode (19 features, identical to shannon-1)** — event type (10 binary), tone, implied_direction, novelty, claim_type (4), specificity, materiality_if_true. Every macro head is a softmax or a signed scalar — argmax for a label, the weighted score for a continuous summary, or the entropy for uncertainty.
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View details ↗g4mer
Biociphers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bioformer-8L-ncbi-disease
bioformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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