Upload files for CTI-BERT

README.md

@@ -1,25 +1,33 @@
 ---
-license: apache-2.0
-language:
-- en
-metrics:
-- accuracy
-- bertscore
-pipeline_tag: text-classification
+tags:
+- generated_from_trainer
+model-index:
+- name: security-bert256-50k
+  results: []
 ---
-CTI-BERT is a pre-trained BERT model for the cybersecurity domain, especially for cyber-threat intelligence extraction and understanding.
-For more details, please refer to [this paper](https://aclanthology.org/2023.emnlp-industry.12.pdf).
 
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-### Training
+# security-bert256-50k
 
-The model was trained on a security text corpus which contains about 1.2 billion words from
-many security news, vulnerability descriptions, books, academic publications, Wikipedia pages, etc.
-The model was pretrained using [the run_mlm script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) with the MLM (masked language modeling) objective. 
+This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
 
+## Model description
 
-#### Model description
-It has a vocabulary of 50,000 tokens and the sequence length of 256.
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0005
@@ -33,18 +41,13 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 10000
 - training_steps: 200000
 
+### Training results
 
-#### Framework versions
+
+
+### Framework versions
 
 - Transformers 4.18.0
 - Pytorch 1.12.1+cu102
 - Datasets 2.4.0
 - Tokenizers 0.12.1
-
-### Intended uses & limitations
-
-You can use the raw model for either masked language modeling or token embedding generation, but it's mostly intended to be fine-tuned on a downstream task, 
-such as sequence classification (NER), text classification or question answering. 
-
-The model has shown improved performance for various cybersecurity-domain tasks. 
-However, it is not inteded to be used as the main model for general-domain documents.

all_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 74.79,
+    "train_loss": 5.0799270787734195e-06,
+    "train_runtime": 2733.1068,
+    "train_samples": 5477256,
+    "train_samples_per_second": 149866.079,
+    "train_steps_per_second": 73.177
+}

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

pytorch_model.bin

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+size 498043179

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": "./tokenizers/security-bert-uncased-50k", "tokenizer_class": "BertTokenizer"}

train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 74.79,
+    "train_loss": 5.0799270787734195e-06,
+    "train_runtime": 2733.1068,
+    "train_samples": 5477256,
+    "train_samples_per_second": 149866.079,
+    "train_steps_per_second": 73.177
+}

training_args.bin

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