emotion-english-roberta-large
j-hartmann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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 2428
This RoBERTa-based model can classify the sentiment of English language text in 3 classes: The model was fine-tuned on 5,304 manually annotated social media posts. The hold-out accuracy is 86.1%. For details on the training approach see Web Appendix F in Hartmann et al. (2021). Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@tum.de with any questions or feedback you may have.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗jailbreak-classifier
jackhhao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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-decider-flash-v1.2-GGUF
Jakevin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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pii
jammmmmm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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-0.5b
jaredpalmer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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-0.6b
jaredpalmer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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-0.8b
jaredpalmerKev-0.8B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is the smallest Kev: a LoRA adapter and a pointer head on Qwen3.5-0.8B-Base that runs on a laptop or a 4 GB GPU. It is intended where memory or cost rules out the larger sizes and the task resembles what it was trained on; out of domain it is markedly less accurate than Kev-4B. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-27b
jaredpalmer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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-4b
jaredpalmerKev-4B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-4B-Base, small enough for one 24 GB GPU or a 32 GB Apple Silicon Mac. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kev-9b
jaredpalmerKev-9B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-9B-Base and fits one 24 GB-class GPU. This card describes version 2, released on 2026-09-30 and included in Kev 1.0. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-sentiment-analysis
jarvisx17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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen2.5-1.5B-apeach
jason9693This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗gelectra-base-injection
JasperLS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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗A model for language detection with support for 45 languages This model was created by fine-tuning bert-base-multilingual-cased on the common language dataset. This dataset has support for 45 languages, which are listed below: This model was evaluated on the test split of the common language dataset, and achieved the following metrics: * Accuracy: 97.8%
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗camembert-ner
Jean-BaptisteAbbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location entity|precision|recall|f1 -|-|-|- PER|0.9372|0.9598|0.9483 ORG|0.8099|0.8265|0.8181 LOC|0.8905|0.9005|0.8955 MISC|0.8175|0.8117|0.8146 For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗camembert-ner-with-dates
Jean-Baptiste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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗roberta-large-financial-news-sentiment-en
Jean-Baptiste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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗roberta-large-ner-english
Jean-BaptisteAbbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location In order to simplify, the prefix B- or I- from original conll2003 was removed. I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size: Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗uplifting-filter-v5
jeergrvgreg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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pii-dual-nym-mmbert-base-v4
jefftherover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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pii-dual-nym-mmbert-base-v5
jefftherover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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.