amanpatkar/distilbert-finetuned-ner

README.md

@@ -1,130 +1,92 @@
----
-base_model: distilbert-base-cased
-datasets:
-- conll2003
-license: mit
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-tags:
-- generated_from_trainer
-model-index:
-- name: distilbert-finetuned-ner
-  results:
-  - task:
-      type: token-classification
-      name: Token Classification
-    dataset:
-      name: conll2003
-      type: conll2003
-      config: conll2003
-      split: validation
-      args: conll2003
-    metrics:
-    - type: precision
-      value: 1
-      name: Precision
-    - type: recall
-      value: 1
-      name: Recall
-    - type: f1
-      value: 1
-      name: F1
-    - type: accuracy
-      value: 1
-      name: Accuracy
----
-
-<!-- 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. -->
-
-# distilbert-finetuned-ner
-
-This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0711
-- Precision: 1.0
-- Recall: 1.0
-- F1: 1.0
-- Accuracy: 1.0
-
-## Model description
-
-The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER) tasks. It is based on the DistilBERT architecture, which is a smaller, faster, and lighter version of BERT. DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it efficient for deployment in production systems.
-
-## 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("amanpatkar/distilbert-finetuned-ner")
-model = AutoModelForTokenClassification.from_pretrained("amanpatkar/distilbert-finetuned-ner")
-
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-example = "My name is Aman Patkar and I live in Gurugram, India."
-
-ner_results = nlp(example)
-print(ner_results)
-```
-
-### Intended Uses
-- Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text.
-- Information Extraction: Automatically identifying and classifying key information in documents.
-- Text Preprocessing: Enhancing text preprocessing for downstream tasks like sentiment analysis and text summarization.
-
-### limitations
-- Domain Specificity: The model is trained on the CoNLL-2003 dataset, which primarily consists of newswire data. Performance may degrade on text from different domains.
-- Language Limitation: This model is trained on English text. It may not perform well on text in other languages.
-- Precision in Complex Sentences: While the model performs well on standard sentences, complex sentence structures or ambiguous contexts might pose challenges.
-
-
-## Training and evaluation data
-
-The model is fine-tuned on the CoNLL-2003 dataset, a widely-used dataset for training and evaluating NER systems. The dataset includes four types of named entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).
-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
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
-| 0.0908        | 1.0   | 1756 | 0.0887          | 1.0       | 1.0    | 1.0 | 1.0      |
-| 0.0467        | 2.0   | 3512 | 0.0713          | 1.0       | 1.0    | 1.0 | 1.0      |
-| 0.0276        | 3.0   | 5268 | 0.0711          | 1.0       | 1.0    | 1.0 | 1.0      |
-
-
-### Framework versions
-
-- Transformers 4.41.2
-- Pytorch 2.3.1
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+---
+license: apache-2.0
+base_model: distilbert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 1.0
+    - name: Recall
+      type: recall
+      value: 1.0
+    - name: F1
+      type: f1
+      value: 1.0
+    - name: Accuracy
+      type: accuracy
+      value: 1.0
+---
+
+<!-- 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. -->
+
+# distilbert-finetuned-ner
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0736
+- Precision: 1.0
+- Recall: 1.0
+- F1: 1.0
+- Accuracy: 1.0
+
+## Model description
+
+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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
+| 0.0911        | 1.0   | 1756 | 0.0875          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0469        | 2.0   | 3512 | 0.0736          | 1.0       | 1.0    | 1.0 | 1.0      |
+| 0.0284        | 3.0   | 5268 | 0.0736          | 1.0       | 1.0    | 1.0 | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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special_tokens_map.json

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-{
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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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-{
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-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "</s>",
-  "legacy": false,
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "</s>",
-  "padding_side": "right",
-  "sp_model_kwargs": {},
-  "tokenizer_class": "LlamaTokenizer",
-  "unk_token": "<unk>",
-  "use_default_system_prompt": false
-}
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+      "single_word": false,
+      "special": true
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+      "content": "[SEP]",
+      "lstrip": false,
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+      "single_word": false,
+      "special": true
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+      "content": "[MASK]",
+      "lstrip": false,
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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