commit from Dat Quoc Nguyen

README.md

@@ -0,0 +1,50 @@
+# <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets 
+
+BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md)  pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the **COVID-19** pandemic. The general architecture and experimental results of BERTweet can be found in our [paper](https://aclanthology.org/2020.emnlp-demos.2/):
+
+    @inproceedings{bertweet,
+    title     = {{BERTweet: A pre-trained language model for English Tweets}},
+    author    = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen},
+    booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
+    pages     = {9--14},
+    year      = {2020}
+    }
+
+**Please CITE** our paper when BERTweet is used to help produce published results or is incorporated into other software.
+
+For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)!
+
+### <a name="models2"></a> Pre-trained models 
+
+
+Model | #params | Arch. | Pre-training data
+---|---|---|---
+`vinai/bertweet-base` | 135M | base | 850M English Tweets (cased)
+`vinai/bertweet-covid19-base-cased` | 135M | base | 23M COVID-19 English Tweets (cased)
+`vinai/bertweet-covid19-base-uncased` | 135M | base | 23M COVID-19 English Tweets (uncased)
+`vinai/bertweet-large` | 355M | large | 873M English Tweets (cased)
+
+
+### <a name="usage2"></a> Example usage 
+
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer 
+
+bertweet = AutoModel.from_pretrained("vinai/bertweet-large")
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large")
+
+# INPUT TWEET IS ALREADY NORMALIZED!
+line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
+
+input_ids = torch.tensor([tokenizer.encode(line)])
+
+with torch.no_grad():
+    features = bertweet(input_ids)  # Models outputs are now tuples
+    
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-large")
+```
+

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.2.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36af147452ccf8846e38283d143efb1e1e003fd28c895008eed78a0a2e47794c
+size 1422008553

tf_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c920d1c6aabf5261079966ccf681b03645b15af9890cf9c06f783049ccb8359
+size 1630210852