nlptown_bert-base-multilingual-uncased-sentiment
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Model card
The complete upstream card, rendered from this payload's README.md — the same hash-verified bytes the torrent distributes. Images and off-site links are removed; the original card on Hugging Face carries them.
language:
- en
- nl
- de
- fr
- it
- es
license: mit
bert-base-multilingual-uncased-sentiment
Visit the NLP Town website for an updated version of this model, with a 40% error reduction on product reviews.
This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks.
Training data
Here is the number of product reviews we used for finetuning the model:
| Language | Number of reviews |
|---|---|
| English | 150k |
| Dutch | 80k |
| German | 137k |
| French | 140k |
| Italian | 72k |
| Spanish | 50k |
Accuracy
The fine-tuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:
- Accuracy (exact) is the exact match for the number of stars.
- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
| Language | Accuracy (exact) | Accuracy (off-by-1) |
|---|---|---|
| English | 67% | 95% |
| Dutch | 57% | 93% |
| German | 61% | 94% |
| French | 59% | 94% |
| Italian | 59% | 95% |
| Spanish | 58% | 95% |
Contact
In addition to this model, NLP Town offers custom models for many languages and NLP tasks.
If you found this model useful, you can buy us a coffee.
Feel free to contact us for questions, feedback and/or requests for similar models.
Magnet link
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magnet:?xt=urn:btih:38c6b2afca6519099f5c7b0f3a24a2cabfc6e3e4&dn=nlptown_bert-base-multilingual-uncased-sentimentOpen magnet in torrent client · infohash 38c6b2afca6519099f5c7b0f3a24a2cabfc6e3e4
Files & hashes
| Path | Size | sha1 | sha256 |
|---|---|---|---|
| README.md | 2.0 KB (2,040 B) | 9f2cb07e7437c4a7ebb004ea66530a95857ef291 | f2719fbf85d3bda03ecd11407429e4b1aa57aace5ddfa1edea668aad50503880 |
| config.json | 953 B (953 B) | 0d9fa8cbf31cb620229494f45b3d2f1cf4610560 | e5387cfa72d4b8f301b7df84365195d25711212b6c3d377cace2b18c3a911a74 |
| model.safetensors | 638.5 MB (669,464,588 B) | fc8ab3aefb2c6204edff8b2d1d9b4c6b9d95d013 | 0435c6c12c80a0a2206137c9fa8f6478fce4f4de88ac3d43d0f0e0da3c8c767b |
| pytorch_model.bin | 638.5 MB (669,491,321 B) | b9a979ecbf9cd3a2b101e5cba8c225147c7eb0fe | e72c9084bf53cee1f722b1acc6f7439cce27e9beabddce447653a1af45405183 |
| special_tokens_map.json | 112 B (112 B) | e7b0375001f109a6b8873d756ad4f7bbb15fbaa5 | 303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3 |
| tokenizer_config.json | 39 B (39 B) | e3656b2367f556bdf85ad4019a052f5ec170069b | bc33e3cc4de965153393572f1a5cd5d2bf18872f8c0a0096ae547beac01cec9c |
| vocab.txt | 851.5 KB (871,891 B) | 03c53303f0ef6535e93372a93be2db71ec46a1e3 | 87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f |
Cite this release
- Canonical URL
- https://aiseedbank.org/models/nlptown_bert-base-multilingual-uncased-sentiment/
- Slug
- nlptown_bert-base-multilingual-uncased-sentiment
- Infohash
- 38c6b2afca6519099f5c7b0f3a24a2cabfc6e3e4
- License
- mit
- Signing key fingerprint
- 85a3b32c3712427b
Every file carries a locally computed sha256 — verify a download against the signed sums: nlptown_bert-base-multilingual-uncased-sentiment.SHA256SUMS (+ minisign signature).
Provenance
| Upstream repository | nlptown/bert-base-multilingual-uncased-sentiment |
|---|---|
| Revision (pinned) | 8f6f4e3a8f70be4b65d3a4a8762b6d781cda240d |
| Fetched at | 2026-09-04T03:19:28Z |
| License at fetch | mit |
| Snapshot tool | huggingface · seedbank 0.1.0 |
Trackers
- udp://announce.aitorrent.org:6969/announce
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✓ verified · rehash-vs-hf-metadata at 2026-09-04T03:19:43Z
mit1.25 GB (1,339,830,944 bytes)transformerspytorchjaxsafetensorsberttext-classificationendpoints_compatible7 languages (tf, en, nl …)