bert-emotion-classifier safetensors download | Hugging Bay

Q: What is this model?

logasanjeev/bert-emotion-classifier is an open-weights language model for text classification published by logasanjeev, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text classification: logasanjeev/bert-emotion-classifier · transformers

Q: Can I use it commercially?

It is released under the mit license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

Hosted here: at least 6 listed files; full count and size unknown. Full repo upstream: 877.4 MB.

Q: What can I download here?

Hosted here: at least 6 listed files; full count and size unknown. Full repo upstream: 877.4 MB. The original release is at https://huggingface.co/logasanjeev/bert-emotion-classifier.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 6 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-logasanjeev-bert-emotion-classifier/safety on Hugging Bay.

Q: Will logasanjeev/bert-emotion-classifier run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.

logasanjeev

bert-emotion-classifier

logasanjeev/bert-emotion-classifier

Model details

text classificationLicense: mitSHA-256 recorded for 7 of 17 filesONNX · safetensors

Content controls: not assessed.

The catalog lists this model for sorting text into categories. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

Can I run it?View as JSON →

Quick answer: compatibility and sources

Can I run bert-emotion-classifier on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 6 hosted files; check the file list for the exact selection. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.

bert-emotion-classifier is an open-weights language model for text classification. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

As of the recorded file availability check on 2026-10-23.

Recorded SHA-256 for model.onnx: 50df8103f1100490223e193457d0c1e13371dc5893d511805baebe5a99799d0f. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated 2026-10-21 · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

Model metadata: As of 2026-09-22 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-23.

Key facts

Catalog record: Summarizes upstream metadata; it is not itself a download or run receipt.
File evidence: Lists exact filenames, availability and any recorded SHA-256 for individual files.
Run evidence: Reports measured results only when a receipt exists for named file bytes and a setup; otherwise unmeasured.
Checksum scope: SHA-256 fingerprints one file’s bytes; it does not prove safety, quality or a multi-file bundle.

Evidence you can inspect (plain text)

This page’s quick answer names the file record for hf-model-logasanjeev-bert-emotion-classifier.
Open /artifact/hf-model-logasanjeev-bert-emotion-classifier/files for this exact filenames, availability and any recorded SHA-256.
Open /evidence/artifacts/hf-model-logasanjeev-bert-emotion-classifier for run-readiness evidence tied to that record; absent receipts stay unmeasured.
Open /api/artifacts/hf-model-logasanjeev-bert-emotion-classifier/card.md for machine-readable source facts (not a runtime test).
A matching checksum on this page fingerprints only the named bytes shown in file evidence.

Unmeasured states are stated explicitly; this page does not invent run scores or compatibility guarantees.

Which date applies to which fact

File availability check: 2026-10-23
Committed page-copy sources: 2026-10-21
Upstream model metadata: Dates on artifact records when shown; not implied by page-copy revision
Run receipts: Only on /evidence pages when recorded; otherwise unmeasured

Page-copy revision (Updated line above) identifies committed text sources, not upstream model edits or test outcomes.

6 hosted of 17 listed

Hashes recorded. Date not recorded.

File	大小	SHA-256	来源	下载
Weights
`model.onnx`	438.3 MB	`50df8103…99799d0f` `50df8103f1100490223e193457d0c1e13371dc5893d511805baebe5a99799d0f`	Upstream only	File metadata
`model.safetensors`	438 MB	`15d5181f…65a186b8` `15d5181ffac4011c167e05d79d0bb0a7eff28875a52dc5673eb65fab65a186b8`	Hosted	下载
Tokenizer & config
`class_wise_f1_plot.png`	46 KB	—	Upstream only	File metadata
`class_wise_metrics.csv`	1.5 KB	—	Upstream only	File metadata
`config.json`	2.14 KB	`ca9ad10f…e654ce86` `ca9ad10f587bd2fc07910e10e0662d5ebf768a032c2586f192ae6054e654ce86`	Hosted	下载
`emotion_labels.json`	333 bytes	—	Upstream only	File metadata
`inference.py`	3.79 KB	—	Upstream only	File metadata
`onnx_inference.py`	3.8 KB	—	Upstream only	File metadata
`optimized_thresholds.json`	947 bytes	—	Upstream only	File metadata
`tokenizer_config.json`	1.27 KB	`7b082b48…171186a0` `7b082b48a08a0b5c5939d99472e33addce3d96202155e33f7928cecd171186a0`	Hosted	下载
`training_curves_plot.png`	55.3 KB	—	Upstream only	File metadata
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	下载
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	下载
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`LICENSE`	1.08 KB	`7f73d082…186611dc` `7f73d08257cf967b7535aaa804e7e86e7535713d487a711074472047186611dc`	Hosted	下载
`README.md`	15.8 KB	—	Upstream only	File metadata

检查下载文件

In the folder where you saved LICENSE, run:

macOS

shasum -a 256 -- 'LICENSE'

Linux

sha256sum -- 'LICENSE'

Windows

Get-FileHash -LiteralPath 'LICENSE' -Algorithm SHA256

Compare the result with the recorded SHA-256.

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