bert base multilingual uncased sentiment
Overview
Highlights
- Supports sentiment analysis across multiple languages
- Standardized rating output for consistent evaluation
- Easy integration via Hugging Face transformers
- MIT licensed for flexible commercial deployment
- Eliminates need for language-specific sentiment pipelines
Usage
# Install Hugging Face transformers
pip install transformers torch# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance：Before downloading, install huggingface_hub with:
pip install -U huggingface_hubCLI Download
Download the full repository
huggingface-cli download nlptown/bert-base-multilingual-uncased-sentimentDownload a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download nlptown/bert-base-multilingual-uncased-sentiment config.json --local-dir ./dirSee the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('nlptown/bert-base-multilingual-uncased-sentiment')Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentimentTo skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentimentModel files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
pip install -U transformers torchLoad the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')Full Documentation
---
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.
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Feel free to contact us for questions, feedback and/or requests for similar models.