Upload 7 files

README.md

@@ -1,3 +1,86 @@
----
-license: mit
----
+---
+language:
+- as 
+- bn
+- gu 
+- hi 
+- kn 
+- ml 
+- mr 
+- or 
+- pa 
+- ta 
+- te 
+license: mit
+datasets:
+- Samanantar
+tags:
+- ner
+- Pytorch
+- transformer
+- multilingual
+- nlp
+- indicnlp
+---
+
+# IndicNER
+IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publicly available Indian NER datasets.
+The 11 languages covered by IndicNER are: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
+
+## Training Corpus
+Our model was trained on a [dataset](https://huggingface.co/datasets/ai4bharat/naamapadam) which we mined from the existing [Samanantar Corpus](https://huggingface.co/datasets/ai4bharat/samanantar). We used a bert-base-multilingual-uncased model as the starting point and then fine-tuned it to the NER dataset mentioned previously.
+
+## Downloads
+Download from this same Huggingface repo.
+
+Update 20 Dec 2022: We released a new paper documenting IndicNER and Naamapadam. We have a different model reported in the paper. We will update the repo here soon with this model.
+
+## Usage
+
+You can use [this Colab notebook](https://colab.research.google.com/drive/1sYa-PDdZQ_c9SzUgnhyb3Fl7j96QBCS8?usp=sharing) for samples on using IndicNER or for finetuning a pre-trained model on Naampadam dataset to build your own NER models.
+
+<!-- citing information -->
+## Citing
+
+If you are using IndicNER, please cite the following article:
+```
+@misc{mhaske2022naamapadam,
+  doi = {10.48550/ARXIV.2212.10168},
+  url = {https://arxiv.org/abs/2212.10168},
+  author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop},
+  title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages}
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+```
+We would like to hear from you if:
+
+- You are using our resources. Please let us know how you are putting these resources to use.
+- You have any feedback on these resources.
+
+
+<!-- License -->
+## License
+
+The IndicNER code (and models) are released under the MIT License.
+
+<!-- Contributors -->
+## Contributors
+ - Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
+ - Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
+ - Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
+ - Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
+ - Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub> 
+ - Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
+ - Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub> 
+
+This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in).
+
+
+<!-- Contact -->
+## Contact
+- Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com))
+- Rudra Murthy V ([rmurthyv@in.ibm.com](mailto:rmurthyv@in.ibm.com))
+  

config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "../base_models/mbert_uncased/checkpoint-1/",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "finetuning_task": "ner",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-LOC",
+    "1": "B-ORG",
+    "2": "B-PER",
+    "3": "I-LOC",
+    "4": "I-ORG",
+    "5": "I-PER",
+    "6": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-LOC": 0,
+    "B-ORG": 1,
+    "B-PER": 2,
+    "I-LOC": 3,
+    "I-ORG": 4,
+    "I-PER": 5,
+    "O": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79e328e6ea15b7058047be65ae8237007ceb8d179bade7c5502390d217c047e1
+size 667173367

special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "../base_models/mbert_uncased/checkpoint-1/", "tokenizer_class": "BertTokenizer"}