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Open-weight model · Text classification
by Vardh Jain vardhjain20/byte-sized-brain-distilbert-imdb
byte-sized-brain-distilbert-imdb is an open-weight model for text classification from Vardh Jain, released under MIT License. It has 512-token context. Its published files total 336.3 MB. It draws 5 downloads a month.
Two ONNX versions of one DistilBERT model that reads an English movie review and says whether it is positive or negative.
By Vardh Jain, published under mit, revision 49836559daf3.
Two ONNX versions of one DistilBERT model that reads an English movie review and says whether it is positive or negative. They were made by Byte-Sized Brain, a project that measures what quantization does to the size, accuracy, speed and memory of neural networks. - distilbertimdbfp32.onnx is the full-precision model, fine-tuned on 3,000 IMDB reviews. - distilbertimdbint8.onnx is the same model after ONNX Runtime dynamic INT8 quantization, which stores the weights as 8-bit numbers. Try both side by side in the hosted demo. The model reads at most 128 tokens. Label 0 is negative and label 1 is positive. Both inputs must be int64, which the tokenizer does not guarantee on every platform.
Two ONNX versions of one DistilBERT model that reads an English movie review and says whether it is positive or negative. They were made by Byte-Sized Brain, a project that measures what quantization does to the size, accuracy, speed and memory of neural networks.
distilbert_imdb_fp32.onnx is the full-precision model, fine-tuned on 3,000 IMDB reviews.distilbert_imdb_int8.onnx is the same model after ONNX Runtime dynamic INT8
quantization, which stores the weights as 8-bit numbers.Try both side by side in the hosted demo.
| File | Size | Accuracy | Mean time per review |
|---|---|---|---|
distilbert_imdb_fp32.onnx |
255.5 MB | 84.6% | 99 ms |
distilbert_imdb_int8.onnx |
64.3 MB | 84.0% | 55 ms |
The model reads at most 128 tokens. Label 0 is negative and label 1 is positive. Both inputs must be int64, which the tokenizer does not guarantee on every platform.
import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
repo = "vardhjain20/byte-sized-brain-distilbert-imdb"
tokenizer = AutoTokenizer.from_pretrained(repo)
session = ort.InferenceSession(hf_hub_download(repo, "distilbert_imdb_int8.onnx"))
enc = tokenizer("A moving, beautifully acted film.", truncation=True, max_length=128,
return_tensors="np")
feed = {name: enc[name].astype(np.int64) for name in ("input_ids", "attention_mask")}
logits = session.run(None, feed)[0]
print(["negative", "positive"][int(np.argmax(logits))])
9 files, 336.3 MB in total. The weights are 2 files totalling 335.4 MB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| distilbert_imdb_fp32.onnx | Weights | 268.0 MB | e04244da5557 |
| distilbert_imdb_int8.onnx | Weights | 67.4 MB | 8dc1f2a70e06 |
| config.json | Configuration | 587 B | — |
| special_tokens_map.json | Configuration | 132 B | — |
| README.md | Documentation | 2.0 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.4 KB | — |
| tokenizer_config.json | Tokenizer | 1.3 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
Released by Vardh Jain through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 335.4 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. byte-sized-brain-distilbert-imdb is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
512 tokens, from the maximum position embeddings in its published configuration.
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.
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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…