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+model.rknn filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+license: mit
+base_model: dslim/bert-base-NER
+tags:
+- rknn
+- rockchip
+- npu
+- rk-transformers
+- rk3588
+library_name: rk-transformers
+model_name: bert-base-NER
+---
+# bert-base-NER (RKNN2)
+
+> This is an RKNN-compatible version of the [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) 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:** [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER)
+- **Target Platform:** rk3588
+- **rknn-toolkit2 Version:** 2.3.2
+- **rk-transformers Version:** 0.3.0
+
+### Available Model Files
+
+| Model File | Optimization Level | Quantization | File Size |
+| :--------- | :----------------- | :----------- | :-------- |
+| [model.rknn](./model.rknn) | 0 | float16 | 212.9 MB |
+
+## Usage
+
+### Installation
+
+Install `rk-transformers` with inference dependencies to use this model:
+
+```bash
+pip install rk-transformers[inference]
+```
+
+#### RK-Transformers API
+
+```python
+from rktransformers import RKModelForTokenClassification
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("rk-transformers/bert-base-NER")
+model = RKModelForTokenClassification.from_pretrained(
+    "rk-transformers/bert-base-NER",
+    platform="rk3588",
+    core_mask="auto",
+)
+
+inputs = tokenizer("My name is Philipp and I live in Germany.", return_tensors="np")
+outputs = model(**inputs)
+logits = outputs.logits
+print(logits.shape)
+```
+
+## Configuration
+
+The full configuration for all exported RKNN models is available in the [config.json](./config.json) file.
+
+</details>
+
+---
+# bert-base-NER
+
+If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks!
+
+<a href="https://www.buymeacoffee.com/dslim" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/arial-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
+
+## Model description
+
+**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). 
+
+Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
+
+If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available. 
+
+### Available NER models 
+| Model Name | Description | Parameters |
+|-------------------|-------------|------------------|
+| [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) **(NEW!)** | Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT | 66M |
+| [bert-large-NER](https://huggingface.co/dslim/bert-large-NER/) | Fine-tuned bert-large-cased - larger model with slightly better performance | 340M |
+| [bert-base-NER](https://huggingface.co/dslim/bert-base-NER)-([uncased](https://huggingface.co/dslim/bert-base-NER-uncased)) | Fine-tuned bert-base, available in both cased and uncased versions | 110M |
+
+
+## Intended uses & limitations
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+#### Limitations and bias
+
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 
+
+## Training data
+
+This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
+
+The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
+
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
+I-MISC | Miscellaneous entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organization right after another organization
+I-ORG |organization
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+
+
+### CoNLL-2003 English Dataset Statistics
+This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. 
+#### # of training examples per entity type
+Dataset|LOC|MISC|ORG|PER
+-|-|-|-|-
+Train|7140|3438|6321|6600
+Dev|1837|922|1341|1842
+Test|1668|702|1661|1617
+#### # of articles/sentences/tokens per dataset
+Dataset |Articles |Sentences |Tokens
+-|-|-|-
+Train |946 |14,987 |203,621
+Dev |216 |3,466 |51,362
+Test |231 |3,684 |46,435
+
+## Training procedure
+
+This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. 
+
+## Eval results
+metric|dev|test
+-|-|-
+f1 |95.1 |91.3
+precision |95.0 |90.7
+recall |95.3 |91.9
+
+The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223).
+
+### BibTeX entry and citation info
+
+```
+@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}
+}
+```
+```
+@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
+    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.  and
+      De Meulder, Fien",
+    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
+    year = "2003",
+    url = "https://www.aclweb.org/anthology/W03-0419",
+    pages = "142--147",
+}
+```

added_tokens.json

@@ -0,0 +1 @@
+{}

config.json

@@ -0,0 +1,95 @@
+{
+  "_num_labels": 9,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-MISC",
+    "2": "I-MISC",
+    "3": "B-PER",
+    "4": "I-PER",
+    "5": "B-ORG",
+    "6": "I-ORG",
+    "7": "B-LOC",
+    "8": "I-LOC"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-LOC": 7,
+    "B-MISC": 1,
+    "B-ORG": 5,
+    "B-PER": 3,
+    "I-LOC": 8,
+    "I-MISC": 2,
+    "I-ORG": 6,
+    "I-PER": 4,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
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+        "quantized_algorithm": "normal",
+        "quantized_dtype": "w8a8",
+        "quantized_hybrid_level": 0,
+        "quantized_method": "channel"
+      },
+      "rktransformers_version": "0.3.0",
+      "single_core_mode": false,
+      "std_values": null,
+      "target_platform": "rk3588",
+      "task": "token-classification",
+      "task_kwargs": null
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.rknn

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+oid sha256:4fccf2a668008908d0167f73ca769f1e29ab68cb29346878388124149cabd5cd
+size 223236674

rknn.json

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+    "model.rknn": {
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+            "input_ids",
+            "attention_mask",
+            "token_type_ids"
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+            "remove_reshape": false,
+            "sparse_infer": false,
+            "model_pruning": false
+        }
+    }
+}

special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "tokenizer_class": "BertTokenizer",
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+}