What can emotion-english-distilroberta-base do?

multi-class emotion classification from english text, batch emotion classification with configurable aggregation, fine-tuning on custom emotion-labeled datasets, emotion prediction with confidence-based filtering and thresholding, deployment to cloud inference endpoints with auto-scaling, emotion prediction with explainability via attention visualization

emotion-english-distilroberta-base

ModelFree

text-classification model by undefined. 7,24,277 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

multi-class emotion classification from english text

Medium confidence

Classifies input text into discrete emotion categories (joy, sadness, anger, fear, surprise, disgust, neutral) using a DistilRoBERTa transformer backbone fine-tuned on social media corpora. The model applies token-level attention mechanisms over the full input sequence and outputs probability distributions across 7 emotion classes, enabling probabilistic emotion detection rather than binary sentiment classification. Architecture uses knowledge distillation from RoBERTa-base to reduce parameters by ~40% while maintaining classification accuracy.

Solves for

Detect dominant emotion in user-generated content (tweets, Reddit posts, reviews) for sentiment analysis pipelinesScore emotional intensity across multiple dimensions to understand nuanced user sentiment beyond positive/negativeBuild emotion-aware chatbots or content recommendation systems that respond contextually to user emotional stateAnalyze customer feedback or support tickets to prioritize responses by emotional urgency (anger/fear vs. neutral)

Best for

NLP engineers building emotion-aware recommendation or moderation systems

Social media analytics teams analyzing user sentiment at scale

Conversational AI developers adding emotional intelligence to chatbots

Requires

Python 3.7+

PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers library)

transformers library 4.0+

Limitations

Trained exclusively on English social media text — performance degrades significantly on formal writing, technical documentation, or non-English languages

7-class taxonomy may not capture fine-grained emotional nuance (e.g., distinguishes anger from frustration only through confidence scores, not separate classes)

Inference latency ~50-150ms per sample on CPU, ~10-20ms on GPU — not suitable for real-time streaming with <5ms SLA

What makes it unique

Uses DistilRoBERTa (knowledge-distilled RoBERTa) rather than full RoBERTa or BERT, reducing model size by ~40% while maintaining 7-class emotion granularity. Fine-tuned specifically on Twitter/Reddit corpora (informal, emoji-rich, sarcasm-heavy text) rather than generic sentiment datasets, enabling better performance on social media edge cases. Implements standard HuggingFace transformers pipeline interface, allowing seamless integration with text-embeddings-inference servers and cloud deployment (Azure, AWS SageMaker).

vs alternatives

Smaller and faster than full RoBERTa-based emotion models (40% fewer parameters) while maintaining competitive accuracy on social media; more emotion-granular than binary sentiment classifiers (7 classes vs. positive/negative); more accessible than proprietary APIs (open-source, no rate limits, can run on-device)

batch emotion classification with configurable aggregation

Medium confidence

Processes multiple text samples in parallel batches (configurable batch size, typically 8-64) and aggregates emotion predictions across documents. Supports multiple aggregation strategies: per-sample class labels with confidence scores, document-level emotion distributions (mean probability across samples), or emotion-weighted summaries for multi-document analysis. Uses HuggingFace DataLoader abstraction to handle variable-length sequences with automatic padding/truncation to 512 tokens.

Solves for

Analyze emotion trends across large corpora (thousands of tweets, reviews, comments) without sequential inference bottlenecksAggregate emotions from multi-turn conversations or multi-document threads to determine overall sentiment trajectoryGenerate emotion statistics for dashboards (e.g., '45% of feedback is angry, 30% neutral, 25% joyful')Prepare emotion-labeled datasets for downstream fine-tuning or evaluation of emotion-aware models

Best for

Data engineers processing large-scale social media or review datasets (100K+ documents)

Analytics teams generating emotion-based KPIs and dashboards

ML teams preparing labeled datasets for emotion-aware model training

Requires

Python 3.7+

PyTorch or TensorFlow (same as single-sample inference)

transformers library with DataLoader support (4.0+)

Limitations

Batch processing introduces latency variance — optimal batch size depends on GPU memory (8-16 on 2GB GPU, 32-64 on 8GB+), requiring tuning per deployment

Aggregation strategies are post-hoc (mean/max pooling) — no learned document-level emotion representation, limiting expressiveness for complex multi-document scenarios

No built-in handling of document-level context — each sample is classified independently, losing inter-document relationships (e.g., emotion evolution in conversation threads)

What makes it unique

Leverages HuggingFace DataLoader abstraction with automatic padding/truncation, enabling efficient batch processing without manual sequence handling. Supports multiple aggregation backends (numpy, pandas, PyArrow) for seamless integration with data pipelines. Compatible with distributed inference frameworks (text-embeddings-inference, vLLM) for horizontal scaling across multiple GPUs/nodes.

vs alternatives

Faster than sequential single-sample inference by 5-10x on GPU due to batch parallelization; more flexible than cloud APIs (no rate limits, configurable batch sizes); integrates natively with Python data science stacks (pandas, polars, Spark) unlike proprietary SaaS solutions

fine-tuning on custom emotion-labeled datasets

Medium confidence

Enables transfer learning by unfreezing and retraining the DistilRoBERTa backbone on custom emotion-labeled datasets with configurable learning rates, epochs, and loss functions. Uses standard PyTorch/TensorFlow training loops with cross-entropy loss for multi-class classification. Supports gradient accumulation for effective larger batch sizes on memory-constrained hardware, and mixed-precision training (FP16) to reduce memory footprint by ~50% while maintaining accuracy.

Solves for

Adapt the base model to domain-specific emotion patterns (e.g., financial news, medical feedback, gaming communities) where social media training data is misalignedImprove performance on underrepresented emotion classes (e.g., 'fear' or 'surprise') by reweighting loss or using focal lossCreate specialized emotion classifiers for non-English languages by fine-tuning on translated or multilingual datasetsReduce inference latency by distilling the model further into a smaller student model via knowledge distillation

Best for

ML engineers with domain expertise and labeled datasets (500+ samples minimum, 5000+ recommended)

Teams building proprietary emotion models for competitive advantage

Researchers experimenting with emotion taxonomy variations or loss functions

Requires

Python 3.7+

PyTorch 1.9+ or TensorFlow 2.4+

transformers library 4.0+

Limitations

Requires labeled training data — no unsupervised fine-tuning capability. Annotation cost is typically $0.10-$0.50 per sample for crowdsourced labels

Risk of catastrophic forgetting — fine-tuning on small datasets (<1000 samples) may degrade performance on original social media domain without careful regularization (learning rate decay, early stopping)

Training time: ~30 minutes on single GPU (8GB) for 5000 samples with batch size 16, ~2-4 hours on CPU — not suitable for rapid iteration without GPU access

What makes it unique

Provides pre-configured training scripts via HuggingFace Trainer API, abstracting away boilerplate PyTorch/TensorFlow code. Supports mixed-precision training (FP16) and gradient accumulation out-of-the-box, reducing memory requirements by 50% without manual implementation. Compatible with distributed training frameworks (Hugging Face Accelerate, PyTorch DDP) for multi-GPU/multi-node scaling without code changes.

vs alternatives

Lower barrier to entry than building custom training loops from scratch; more flexible than cloud fine-tuning services (no vendor lock-in, full control over hyperparameters); faster iteration than retraining from scratch due to transfer learning initialization

emotion prediction with confidence-based filtering and thresholding

Medium confidence

Returns emotion predictions with associated confidence scores (softmax probabilities) and supports confidence-based filtering to exclude low-confidence predictions. Enables threshold-based decision rules (e.g., 'only flag as angry if confidence > 0.85') and abstention strategies (e.g., 'return neutral if top-2 emotions are within 5% probability'). Useful for downstream systems requiring high-precision predictions or explicit uncertainty quantification.

Solves for

Filter out ambiguous emotion predictions to improve downstream decision quality (e.g., only escalate support tickets with high-confidence anger)Implement confidence-based routing (high-confidence predictions → automated response, low-confidence → human review)Generate uncertainty estimates for model monitoring and anomaly detection (e.g., alert when confidence drops below historical baseline)Build ensemble predictions by combining this model's confidence scores with other emotion classifiers

Best for

Content moderation teams requiring high-precision emotion detection (false positives are costly)

Customer support systems routing tickets based on emotional urgency with human fallback

Model monitoring/observability teams tracking prediction confidence as a proxy for data drift

Requires

Python 3.7+

transformers library 4.0+

PyTorch or TensorFlow

Limitations

Confidence scores are not calibrated — raw softmax probabilities may not reflect true misclassification likelihood, especially on out-of-distribution text. Requires post-hoc calibration (temperature scaling, Platt scaling) for reliable uncertainty

No principled uncertainty quantification — softmax confidence is a point estimate, not a Bayesian posterior. Bayesian variants (MC Dropout, ensemble methods) would provide better uncertainty but are not built-in

Threshold selection is manual and dataset-dependent — optimal confidence threshold varies by use case and requires validation set tuning, no automatic threshold optimization

What makes it unique

Exposes raw softmax probabilities and logits alongside class predictions, enabling downstream confidence-based filtering without model modification. Supports multiple confidence aggregation strategies (max probability, entropy, margin between top-2 classes) for flexible uncertainty quantification. Compatible with standard calibration libraries (scikit-learn, netcal) for post-hoc confidence calibration if needed.

vs alternatives

More transparent than black-box APIs that return only class labels; enables custom confidence thresholding without retraining; integrates with standard uncertainty quantification workflows unlike proprietary emotion APIs

deployment to cloud inference endpoints with auto-scaling

Medium confidence

Model is compatible with HuggingFace Inference Endpoints and text-embeddings-inference (TEI) servers, enabling serverless or containerized deployment with automatic scaling. Supports both REST API and gRPC interfaces for low-latency inference. Deployments automatically handle batching, caching, and load balancing across multiple replicas. Compatible with Azure ML, AWS SageMaker, and Kubernetes for enterprise deployment patterns.

Solves for

Deploy emotion classification as a scalable microservice without managing infrastructure (serverless via HuggingFace Endpoints)Integrate emotion detection into production ML pipelines with auto-scaling based on request volumeBuild real-time emotion analysis APIs with sub-100ms latency SLA via optimized inference servers (TEI, vLLM)Enable multi-tenant emotion classification with resource isolation and billing per API key

Best for

DevOps/MLOps engineers deploying models to production

Startups and small teams avoiding infrastructure management overhead

Enterprises requiring compliance (HIPAA, GDPR) with on-premise or private cloud deployment

Requires

HuggingFace account with API token

Docker (for self-hosted TEI deployment)

Kubernetes cluster (optional, for on-premise deployment)

Limitations

HuggingFace Endpoints pricing: ~$0.06/hour for CPU, ~$0.30/hour for GPU (as of 2024) — cost scales with uptime, not usage, making it expensive for bursty workloads

Cold start latency: ~2-5 seconds for serverless endpoints on first request after idle period; warm inference ~50-150ms on CPU, ~10-20ms on GPU

Batch size optimization is automatic but opaque — users cannot directly control batching behavior, limiting fine-tuning for specific latency/throughput tradeoffs

What makes it unique

Native integration with HuggingFace Inference Endpoints (no custom code required) and text-embeddings-inference (TEI) for optimized inference. Supports multiple deployment backends (serverless, containerized, Kubernetes) without model modification. Includes built-in batching and caching at the inference server level, reducing per-request latency by 3-5x compared to single-sample inference.

vs alternatives

Easier deployment than custom FastAPI/Flask servers (no boilerplate code); cheaper than proprietary emotion APIs for high-volume use cases; more flexible than cloud-only solutions (can run on-premise via TEI/Kubernetes)

emotion prediction with explainability via attention visualization

Medium confidence

Extracts and visualizes token-level attention weights from the transformer to identify which words/phrases most influenced the emotion prediction. Uses attention head aggregation (averaging attention across heads and layers) to produce interpretable saliency maps. Enables generation of highlighted text showing emotion-driving tokens, useful for understanding model decisions and debugging misclassifications.

Solves for

Debug model misclassifications by identifying which tokens the model attended to (e.g., 'why did it classify this as angry?')Generate human-interpretable explanations for end-users (highlight words that triggered emotion detection)Validate model behavior on edge cases (e.g., sarcasm, negation) by examining attention patternsBuild trust in automated emotion-based decisions by showing evidence for classification

Best for

ML researchers and practitioners debugging model behavior

Content moderation teams explaining decisions to users

Compliance/audit teams documenting model reasoning for regulatory requirements

Requires

Python 3.7+

transformers library 4.0+ with attention output enabled

PyTorch or TensorFlow

Limitations

Attention is not a true explanation — attention weights correlate with model decisions but don't causally explain them. High attention to a token doesn't guarantee it caused the prediction

Attention aggregation is lossy — averaging across 12 layers and 12 heads discards fine-grained information about which specific layers/heads drove the decision

Visualization is post-hoc and non-interactive — no built-in tools for interactive exploration or counterfactual analysis (e.g., 'what if I remove this token?')

What makes it unique

Leverages DistilRoBERTa's multi-head attention mechanism (12 heads, 6 layers) to extract fine-grained token importance scores. Supports multiple aggregation strategies (mean, max, gradient-based) for attention visualization. Compatible with standard explainability libraries (captum, transformers-interpret) for advanced analysis (integrated gradients, SHAP values).

vs alternatives

More interpretable than black-box emotion APIs; faster to compute than gradient-based explanations (SHAP, integrated gradients); more transparent than confidence scores alone

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with emotion-english-distilroberta-base, ranked by overlap. Discovered automatically through the match graph.

Model45

distilbert-base-uncased-emotion

text-classification model by undefined. 7,39,682 downloads.

six-class emotion classification from textbatch emotion inference with multi-backend supportzero-shot emotion transfer via fine-tuningemotion embedding extraction for downstream tasks

4 shared capabilities

Model44

facial_emotions_image_detection

image-classification model by undefined. 6,04,041 downloads.

batch emotion classification with confidence scoringemotion class label mapping and interpretation

2 shared capabilities

Repository26

speechbrain

All-in-one speech toolkit in pure Python and Pytorch

emotion recognition from speech with multi-class classification

1 shared capability

Model21

Mistral: Mistral Small 3

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...

sentiment analysis and emotion detection from text

1 shared capability

Framework43

Flair

PyTorch NLP framework with contextual embeddings.

text classification with document-level embeddings and dense layers

1 shared capability

Product20

iSpeech

[Review](https://theresanai.com/ispeech) - A versatile solution for corporate applications with support for a wide array of languages and voices.

voice emotion and sentiment detection from speech

1 shared capability

Best For

✓NLP engineers building emotion-aware recommendation or moderation systems
✓Social media analytics teams analyzing user sentiment at scale
✓Conversational AI developers adding emotional intelligence to chatbots
✓Content moderation teams flagging high-emotion content (anger, fear)
✓Data engineers processing large-scale social media or review datasets (100K+ documents)
✓Analytics teams generating emotion-based KPIs and dashboards
✓ML teams preparing labeled datasets for emotion-aware model training
✓Batch processing pipelines (Spark, Airflow, Kubernetes jobs) requiring high throughput

Known Limitations

⚠Trained exclusively on English social media text — performance degrades significantly on formal writing, technical documentation, or non-English languages
⚠7-class taxonomy may not capture fine-grained emotional nuance (e.g., distinguishes anger from frustration only through confidence scores, not separate classes)
⚠Inference latency ~50-150ms per sample on CPU, ~10-20ms on GPU — not suitable for real-time streaming with <5ms SLA
⚠Context window limited to 512 tokens (standard BERT/RoBERTa constraint) — long-form documents require chunking or summarization
⚠No confidence calibration — raw logits may not reflect true probability of misclassification, especially on out-of-distribution text
⚠Batch processing introduces latency variance — optimal batch size depends on GPU memory (8-16 on 2GB GPU, 32-64 on 8GB+), requiring tuning per deployment

Requirements

Python 3.7+PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers library)transformers library 4.0+~350MB disk space for model weights (DistilRoBERTa-base is ~270MB + config/tokenizer)4GB+ RAM for batch inference, 8GB+ recommended for fine-tuningPyTorch or TensorFlow (same as single-sample inference)transformers library with DataLoader support (4.0+)GPU recommended for batch size >32 (CPU inference ~5-10x slower)

Input / Output

Accepts: raw text (string), pre-tokenized text (list of tokens), batched text (list of strings up to 512 tokens each), list of text strings (variable length, auto-padded to 512 tokens), CSV/JSON files with text column, streaming data (via generator functions), CSV with columns: [text, emotion_label], JSON Lines format: {"text": "...", "label": "joy"}, HuggingFace Dataset objects, text string, batched text (list of strings), JSON payload: {"inputs": "text to classify"}, batched JSON: {"inputs": ["text1", "text2", ...]}, pre-tokenized text

Produces: emotion class labels (string: 'joy', 'sadness', 'anger', 'fear', 'surprise', 'disgust', 'neutral'), logits (7-dimensional float vector, raw model outputs), probabilities (7-dimensional float vector, softmax-normalized scores summing to 1.0), DataFrame with columns: [text, emotion_label, confidence_score, logits_vector], aggregated statistics: {emotion: count, emotion: percentage}, emotion distribution vectors (7-dim probability per document), fine-tuned model weights (PyTorch .pt or TensorFlow SavedModel format), training metrics: {loss, accuracy, precision, recall, f1 per epoch}, validation results on held-out test set, emotion label (string), confidence score (float, 0.0-1.0), full probability distribution (7-dim vector), filtered predictions (None if below threshold), JSON response: {"label": "joy", "score": 0.95}, batched response: [{"label": "joy", "score": 0.95}, ...], attention weight matrix (sequence_length x num_heads x num_layers), aggregated saliency scores per token (sequence_length,), HTML/image visualization with highlighted tokens

UnfragileRank

Adoption72%(40% weight)

Quality22%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

6 capabilities

Visit emotion-english-distilroberta-base→

Model Details

huggingface

Provider

transformers

Architecture

724,277

Downloads

Tasks

text-classification

About

j-hartmann/emotion-english-distilroberta-base — a text-classification model on HuggingFace with 7,24,277 downloads

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Data Sources

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Capabilities6 decomposed

multi-class emotion classification from english text

Medium confidence

Solves for

Best for

NLP engineers building emotion-aware recommendation or moderation systems

Social media analytics teams analyzing user sentiment at scale

Conversational AI developers adding emotional intelligence to chatbots

Requires

Python 3.7+

PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers library)

transformers library 4.0+

Limitations

Trained exclusively on English social media text — performance degrades significantly on formal writing, technical documentation, or non-English languages

7-class taxonomy may not capture fine-grained emotional nuance (e.g., distinguishes anger from frustration only through confidence scores, not separate classes)

Inference latency ~50-150ms per sample on CPU, ~10-20ms on GPU — not suitable for real-time streaming with <5ms SLA

What makes it unique

vs alternatives

batch emotion classification with configurable aggregation

Medium confidence

Solves for

Best for

Data engineers processing large-scale social media or review datasets (100K+ documents)

Analytics teams generating emotion-based KPIs and dashboards

ML teams preparing labeled datasets for emotion-aware model training

Requires

Python 3.7+

PyTorch or TensorFlow (same as single-sample inference)

transformers library with DataLoader support (4.0+)

Limitations

Batch processing introduces latency variance — optimal batch size depends on GPU memory (8-16 on 2GB GPU, 32-64 on 8GB+), requiring tuning per deployment

Aggregation strategies are post-hoc (mean/max pooling) — no learned document-level emotion representation, limiting expressiveness for complex multi-document scenarios

No built-in handling of document-level context — each sample is classified independently, losing inter-document relationships (e.g., emotion evolution in conversation threads)

What makes it unique

vs alternatives

fine-tuning on custom emotion-labeled datasets

Medium confidence

Solves for

Best for

ML engineers with domain expertise and labeled datasets (500+ samples minimum, 5000+ recommended)

Teams building proprietary emotion models for competitive advantage

Researchers experimenting with emotion taxonomy variations or loss functions

Requires

Python 3.7+

PyTorch 1.9+ or TensorFlow 2.4+

transformers library 4.0+

Limitations

Requires labeled training data — no unsupervised fine-tuning capability. Annotation cost is typically $0.10-$0.50 per sample for crowdsourced labels

Training time: ~30 minutes on single GPU (8GB) for 5000 samples with batch size 16, ~2-4 hours on CPU — not suitable for rapid iteration without GPU access

What makes it unique

vs alternatives

emotion prediction with confidence-based filtering and thresholding

Medium confidence

Solves for

Best for

Content moderation teams requiring high-precision emotion detection (false positives are costly)

Customer support systems routing tickets based on emotional urgency with human fallback

Model monitoring/observability teams tracking prediction confidence as a proxy for data drift

Requires

Python 3.7+

transformers library 4.0+

PyTorch or TensorFlow

Limitations

Threshold selection is manual and dataset-dependent — optimal confidence threshold varies by use case and requires validation set tuning, no automatic threshold optimization

What makes it unique

vs alternatives

deployment to cloud inference endpoints with auto-scaling

Medium confidence

Solves for

Best for

DevOps/MLOps engineers deploying models to production

Startups and small teams avoiding infrastructure management overhead

Enterprises requiring compliance (HIPAA, GDPR) with on-premise or private cloud deployment

Requires

HuggingFace account with API token

Docker (for self-hosted TEI deployment)

Kubernetes cluster (optional, for on-premise deployment)

Limitations

HuggingFace Endpoints pricing: ~$0.06/hour for CPU, ~$0.30/hour for GPU (as of 2024) — cost scales with uptime, not usage, making it expensive for bursty workloads

Cold start latency: ~2-5 seconds for serverless endpoints on first request after idle period; warm inference ~50-150ms on CPU, ~10-20ms on GPU

Batch size optimization is automatic but opaque — users cannot directly control batching behavior, limiting fine-tuning for specific latency/throughput tradeoffs

What makes it unique

vs alternatives

emotion prediction with explainability via attention visualization

Medium confidence

Solves for

Best for

ML researchers and practitioners debugging model behavior

Content moderation teams explaining decisions to users

Compliance/audit teams documenting model reasoning for regulatory requirements

Requires

Python 3.7+

transformers library 4.0+ with attention output enabled

PyTorch or TensorFlow

Limitations

Attention is not a true explanation — attention weights correlate with model decisions but don't causally explain them. High attention to a token doesn't guarantee it caused the prediction

Attention aggregation is lossy — averaging across 12 layers and 12 heads discards fine-grained information about which specific layers/heads drove the decision

Visualization is post-hoc and non-interactive — no built-in tools for interactive exploration or counterfactual analysis (e.g., 'what if I remove this token?')

What makes it unique

vs alternatives

More interpretable than black-box emotion APIs; faster to compute than gradient-based explanations (SHAP, integrated gradients); more transparent than confidence scores alone

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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emotion-english-distilroberta-base

Capabilities6 decomposed

multi-class emotion classification from english text

batch emotion classification with configurable aggregation

fine-tuning on custom emotion-labeled datasets

emotion prediction with confidence-based filtering and thresholding

deployment to cloud inference endpoints with auto-scaling

emotion prediction with explainability via attention visualization

Related Artifactssharing capabilities

distilbert-base-uncased-emotion

facial_emotions_image_detection

speechbrain

Mistral: Mistral Small 3

Flair

iSpeech

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to emotion-english-distilroberta-base

Are you the builder of emotion-english-distilroberta-base?

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Data Sources

emotion-english-distilroberta-base

Capabilities6 decomposed

multi-class emotion classification from english text

batch emotion classification with configurable aggregation

fine-tuning on custom emotion-labeled datasets

emotion prediction with confidence-based filtering and thresholding

deployment to cloud inference endpoints with auto-scaling

emotion prediction with explainability via attention visualization

Related Artifactssharing capabilities

distilbert-base-uncased-emotion

facial_emotions_image_detection

speechbrain

Mistral: Mistral Small 3

Flair

iSpeech

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to emotion-english-distilroberta-base

Are you the builder of emotion-english-distilroberta-base?

Get the weekly brief

Data Sources