Model added

README.md

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 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+- f1
+language:
+- en
+widget:
+- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
+- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
+- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
+- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
+model-index:
+- name: bert-uncased-keyword-extractor
+  results: []
 ---
+
+<!-- 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. -->
+
+# bert-uncased-keyword-extractor
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1247
+- Precision: 0.8547
+- Recall: 0.8825
+- Accuracy: 0.9741
+- F1: 0.8684
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+### Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("token-classification", model="Azma-AI/bert-uncased-keyword-extractor")
+
+### Load model directly
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Azma-AI/bert-uncased-keyword-extractor")
+model = AutoModelForTokenClassification.from_pretrained("Azma-AI/bert-uncased-keyword-extractor")
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 8
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
+| 0.165         | 1.0   | 1875  | 0.1202          | 0.7109    | 0.7766 | 0.9505   | 0.7423 |
+| 0.1211        | 2.0   | 3750  | 0.1011          | 0.7801    | 0.8186 | 0.9621   | 0.7989 |
+| 0.0847        | 3.0   | 5625  | 0.0945          | 0.8292    | 0.8044 | 0.9667   | 0.8166 |
+| 0.0614        | 4.0   | 7500  | 0.0927          | 0.8409    | 0.8524 | 0.9711   | 0.8466 |
+| 0.0442        | 5.0   | 9375  | 0.1057          | 0.8330    | 0.8738 | 0.9712   | 0.8529 |
+| 0.0325        | 6.0   | 11250 | 0.1103          | 0.8585    | 0.8743 | 0.9738   | 0.8663 |
+| 0.0253        | 7.0   | 13125 | 0.1204          | 0.8453    | 0.8825 | 0.9735   | 0.8635 |
+| 0.0203        | 8.0   | 15000 | 0.1247          | 0.8547    | 0.8825 | 0.9741   | 0.8684 |
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.2
+- Tokenizers 0.12.1

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-KEY",
+    "2": "I-KEY"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-KEY": 1,
+    "I-KEY": 2,
+    "O": 0
+  },
+  "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": "4.19.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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gitignore.txt

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special_tokens_map.json

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tokenizer_config.json

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