Search public pages, research tools, and SAVRN solutions.
Open-weight model · Text classification
by Can Jin cannj/2026-24679-animal-description-distilbert
2026-24679-animal-description-distilbert is an open-weight model for text classification from Can Jin. Its published files total 266.3 MB.
This model classifies short English animal descriptions as a mammal, bird, or reptile. The model was created for Homework 2, Problem 3. It fine-tunes distilbert-base-uncased using AutoGluon MultiModal.
This model classifies short English animal descriptions as a mammal, bird, or reptile. The model was created for Homework 2, Problem 3. It fine-tunes distilbert-base-uncased using AutoGluon MultiModal. The published dataset splits were used without repartitioning: The training split contains 70 original descriptions and 1000 augmented descriptions. The validation and test splits contain only original descriptions. Related examples were kept within the same split using the dataset's parentid and animalrow provenance fields. The model was trained for a maximum of five epochs. The checkpoint with the best validation accuracy was retained. Validation accuracy was 0.6000 and validation weighted…
Excerpt from the card by Can Jin.
17 files, 266.3 MB in total. The weights are 1 file totalling 265.5 MB in ckpt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| automm/model.ckpt | Weights | 265.5 MB | 982289c9a2c9 |
| artifacts/test_metrics.json | Configuration | 237 B | — |
| automm/assets.json | Configuration | 512 B | — |
| automm/config.yaml | Configuration | 2.9 KB | — |
| automm/model_notes.json | Configuration | 1.7 KB | — |
| README.md | Documentation | 5.8 KB | — |
| artifacts/holdout_error_analysis.csv | Other | 3.9 KB | — |
| artifacts/holdout_predictions.csv | Other | 8.1 KB | — |
| artifacts/test_classification_report.csv | Other | 369 B | — |
| artifacts/test_confusion_matrix.png | Other | 52.5 KB | — |
| automm/data_processors.pkl | Other | 13.5 KB | 86e0a829dbda |
| automm/df_preprocessor.pkl | Other | 13.5 KB | a90272fc17f4 |
| automm/eval_metric.pkl | Other | 220 B | b0024ce25367 |
| automm/requirements.txt | Other | 150 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| automm/hf_text/tokenizer.json | Tokenizer | 711.4 KB | — |
| automm/hf_text/tokenizer_config.json | Tokenizer | 351 B | — |
Released by Can Jin through its official repository on Hugging Face.
Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Animal Description Text Dataset | Task Animal Type ClassificationMetric Test AccuracyComparison conditions not established | 0.466667 | cannj Publisher reported |
Evaluated revision not stated | — |
| Animal Description Text Dataset | Task Animal Type ClassificationMetric Test Macro F1Comparison conditions not established | 0.212121 | cannj Publisher reported |
Evaluated revision not stated | — |
| Precision | Weights in memory |
|---|---|
| As published | 265.5 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. 0 -> Negative; 1 -> Neutral; 2 -> Positive This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.
FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. More technical details on FinBERT: Click Link This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using FinBERT for financial tone analysis, give it a try. If you use the model in your academic work, please cite the following paper: Huang, Allen H.…
https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). This model has been integrated into the TweetNLP library.
The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context. Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available. It is also noteworthy to mention…
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 2) disgust 3) fear 4) joy 5) neutral 6) sadness 7) surprise The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version. a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: b) Run emotion model on multiple examples and full datasets (e.g.,.csv files) on Google Colab: Please reach out to [email protected] if you have any…