| --- |
| language: en |
| tags: |
| - exbert |
| - rknn |
| - rockchip |
| - npu |
| - rk-transformers |
| - rk3588 |
| license: apache-2.0 |
| model_name: bert-base-uncased |
| base_model: google-bert/bert-base-uncased |
| library_name: rk-transformers |
| --- |
| # bert-base-uncased (RKNN2) |
|
|
| > This is an RKNN-compatible version of the [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) model. It has been optimized for Rockchip NPUs using the [rk-transformers](https://github.com/emapco/rk-transformers) library. |
|
|
| <details><summary>Click to see the RKNN model details and usage examples</summary> |
|
|
| ## Model Details |
|
|
| - **Original Model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) |
| - **Target Platform:** rk3588 |
| - **rknn-toolkit2 Version:** 2.3.2 |
| - **rk-transformers Version:** 0.1.0 |
|
|
| ### Available Model Files |
|
|
| | Model File | Optimization Level | Quantization | File Size | |
| | :--------- | :----------------- | :----------- | :-------- | |
| | [model.rknn](./model.rknn) | 0 | float16 | 261.1 MB | |
| | [model_b1_s256.rknn](./model_b1_s256.rknn) | 0 | float16 | 258.4 MB | |
| | [model_b4_s256.rknn](./model_b4_s256.rknn) | 0 | float16 | 270.3 MB | |
| | [model_b4_s512.rknn](./model_b4_s512.rknn) | 0 | float16 | 280.5 MB | |
| | [rknn/model_o1.rknn](./rknn/model_o1.rknn) | 1 | float16 | 261.1 MB | |
| | [rknn/model_o2.rknn](./rknn/model_o2.rknn) | 2 | float16 | 261.1 MB | |
| | [rknn/model_o3.rknn](./rknn/model_o3.rknn) | 3 | float16 | 261.1 MB | |
| | [rknn/model_w8a8.rknn](./rknn/model_w8a8.rknn) | 0 | w8a8 | 133.6 MB | |
|
|
| ## Usage |
|
|
| ### Installation |
|
|
| Install `rk-transformers` to use this model: |
|
|
| ```bash |
| pip install rk-transformers |
| ``` |
|
|
| #### RKTransformers API |
|
|
| ```python |
| from rktransformers import RKRTModelForFeatureExtraction |
| from transformers import AutoTokenizer |
| |
| # Load tokenizer and model |
| tokenizer = AutoTokenizer.from_pretrained("rk-transformers/bert-base-uncased") |
| model = RKRTModelForFeatureExtraction.from_pretrained( |
| "rk-transformers/bert-base-uncased", |
| platform="rk3588", |
| core_mask="auto", |
| ) |
| |
| # Tokenize and run inference |
| inputs = tokenizer( |
| ["Sample text for encoding"], |
| padding="max_length", |
| max_length=256, |
| truncation=True, |
| return_tensors="np" |
| ) |
| |
| outputs = model(**inputs) |
| print(outputs.shape) |
| |
| # Load specific optimized/quantized model file |
| model = RKRTModelForFeatureExtraction.from_pretrained( |
| "rk-transformers/bert-base-uncased", |
| platform="rk3588", |
| file_name="rknn/model_w8a8.rknn" |
| ) |
| ``` |
|
|
| ## Configuration |
|
|
| The full configuration for all exported RKNN models is available in the [rknn.json](./rknn.json) file. |
|
|
| </details> |
|
|
| # BERT base model (uncased) |
|
|
| Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in |
| [this paper](https://arxiv.org/abs/1810.04805) and first released in |
| [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference |
| between english and English. |
|
|
| Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by |
| the Hugging Face team. |
|
|
| ## Model description |
|
|
| BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it |
| was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of |
| publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it |
| was pretrained with two objectives: |
|
|
| - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run |
| the entire masked sentence through the model and has to predict the masked words. This is different from traditional |
| recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like |
| GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the |
| sentence. |
| - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes |
| they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to |
| predict if the two sentences were following each other or not. |
|
|
| This way, the model learns an inner representation of the English language that can then be used to extract features |
| useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard |
| classifier using the features produced by the BERT model as inputs. |
|
|
| ## Model variations |
|
|
| BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers. |
| Chinese and multilingual uncased and cased versions followed shortly after. |
| Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models. |
| Other 24 smaller models are released afterward. |
|
|
| The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github. |
|
|
| | Model | #params | Language | |
| |------------------------|--------------------------------|-------| |
| | [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English | |
| | [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub |
| | [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English | |
| | [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English | |
| | [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese | |
| | [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple | |
| | [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English | |
| | [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English | |
|
|
| ## Intended uses & limitations |
|
|
| You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to |
| be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for |
| fine-tuned versions of a task that interests you. |
|
|
| Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) |
| to make decisions, such as sequence classification, token classification or question answering. For tasks such as text |
| generation you should look at model like GPT2. |
|
|
| ### How to use |
|
|
| You can use this model directly with a pipeline for masked language modeling: |
|
|
| ```python |
| >>> from transformers import pipeline |
| >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') |
| >>> unmasker("Hello I'm a [MASK] model.") |
| |
| [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", |
| 'score': 0.1073106899857521, |
| 'token': 4827, |
| 'token_str': 'fashion'}, |
| {'sequence': "[CLS] hello i'm a role model. [SEP]", |
| 'score': 0.08774490654468536, |
| 'token': 2535, |
| 'token_str': 'role'}, |
| {'sequence': "[CLS] hello i'm a new model. [SEP]", |
| 'score': 0.05338378623127937, |
| 'token': 2047, |
| 'token_str': 'new'}, |
| {'sequence': "[CLS] hello i'm a super model. [SEP]", |
| 'score': 0.04667217284440994, |
| 'token': 3565, |
| 'token_str': 'super'}, |
| {'sequence': "[CLS] hello i'm a fine model. [SEP]", |
| 'score': 0.027095865458250046, |
| 'token': 2986, |
| 'token_str': 'fine'}] |
| ``` |
|
|
| Here is how to use this model to get the features of a given text in PyTorch: |
|
|
| ```python |
| from transformers import BertTokenizer, BertModel |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
| model = BertModel.from_pretrained("bert-base-uncased") |
| text = "Replace me by any text you'd like." |
| encoded_input = tokenizer(text, return_tensors='pt') |
| output = model(**encoded_input) |
| ``` |
|
|
| and in TensorFlow: |
|
|
| ```python |
| from transformers import BertTokenizer, TFBertModel |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
| model = TFBertModel.from_pretrained("bert-base-uncased") |
| text = "Replace me by any text you'd like." |
| encoded_input = tokenizer(text, return_tensors='tf') |
| output = model(encoded_input) |
| ``` |
|
|
| ### Limitations and bias |
|
|
| Even if the training data used for this model could be characterized as fairly neutral, this model can have biased |
| predictions: |
|
|
| ```python |
| >>> from transformers import pipeline |
| >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') |
| >>> unmasker("The man worked as a [MASK].") |
| |
| [{'sequence': '[CLS] the man worked as a carpenter. [SEP]', |
| 'score': 0.09747550636529922, |
| 'token': 10533, |
| 'token_str': 'carpenter'}, |
| {'sequence': '[CLS] the man worked as a waiter. [SEP]', |
| 'score': 0.0523831807076931, |
| 'token': 15610, |
| 'token_str': 'waiter'}, |
| {'sequence': '[CLS] the man worked as a barber. [SEP]', |
| 'score': 0.04962705448269844, |
| 'token': 13362, |
| 'token_str': 'barber'}, |
| {'sequence': '[CLS] the man worked as a mechanic. [SEP]', |
| 'score': 0.03788609802722931, |
| 'token': 15893, |
| 'token_str': 'mechanic'}, |
| {'sequence': '[CLS] the man worked as a salesman. [SEP]', |
| 'score': 0.037680890411138535, |
| 'token': 18968, |
| 'token_str': 'salesman'}] |
| |
| >>> unmasker("The woman worked as a [MASK].") |
| |
| [{'sequence': '[CLS] the woman worked as a nurse. [SEP]', |
| 'score': 0.21981462836265564, |
| 'token': 6821, |
| 'token_str': 'nurse'}, |
| {'sequence': '[CLS] the woman worked as a waitress. [SEP]', |
| 'score': 0.1597415804862976, |
| 'token': 13877, |
| 'token_str': 'waitress'}, |
| {'sequence': '[CLS] the woman worked as a maid. [SEP]', |
| 'score': 0.1154729500412941, |
| 'token': 10850, |
| 'token_str': 'maid'}, |
| {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', |
| 'score': 0.037968918681144714, |
| 'token': 19215, |
| 'token_str': 'prostitute'}, |
| {'sequence': '[CLS] the woman worked as a cook. [SEP]', |
| 'score': 0.03042375110089779, |
| 'token': 5660, |
| 'token_str': 'cook'}] |
| ``` |
|
|
| This bias will also affect all fine-tuned versions of this model. |
|
|
| ## Training data |
|
|
| The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 |
| unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and |
| headers). |
|
|
| ## Training procedure |
|
|
| ### Preprocessing |
|
|
| The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are |
| then of the form: |
|
|
| ``` |
| [CLS] Sentence A [SEP] Sentence B [SEP] |
| ``` |
|
|
| With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in |
| the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a |
| consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two |
| "sentences" has a combined length of less than 512 tokens. |
|
|
| The details of the masking procedure for each sentence are the following: |
| - 15% of the tokens are masked. |
| - In 80% of the cases, the masked tokens are replaced by `[MASK]`. |
| - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. |
| - In the 10% remaining cases, the masked tokens are left as is. |
|
|
| ### Pretraining |
|
|
| The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size |
| of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer |
| used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, |
| learning rate warmup for 10,000 steps and linear decay of the learning rate after. |
|
|
| ## Evaluation results |
|
|
| When fine-tuned on downstream tasks, this model achieves the following results: |
|
|
| Glue test results: |
|
|
| | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |
| |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| |
| | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | |
|
|
|
|
| ### BibTeX entry and citation info |
|
|
| ```bibtex |
| @article{DBLP:journals/corr/abs-1810-04805, |
| author = {Jacob Devlin and |
| Ming{-}Wei Chang and |
| Kenton Lee and |
| Kristina Toutanova}, |
| title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language |
| Understanding}, |
| journal = {CoRR}, |
| volume = {abs/1810.04805}, |
| year = {2018}, |
| url = {http://arxiv.org/abs/1810.04805}, |
| archivePrefix = {arXiv}, |
| eprint = {1810.04805}, |
| timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, |
| biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| ``` |
|
|
| <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> |
| <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
| </a> |
| |
