3000 steps checkpoint

README.md

@@ -0,0 +1,65 @@
+---
+license: apache-2.0
+language:
+- en
+- vi
+metrics:
+- f1
+base_model:
+- distilbert/distilbert-base-multilingual-cased
+pipeline_tag: fill-mask
+tags:
+- finance
+- esg
+- text-classification
+- fill-mask
+- bert
+library_name: transformers
+datasets:
+- nguyen599/ViEn-ESG-100
+widget:
+- text: "Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation."
+---
+
+ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. MaskESG-DistilBERT-base is a [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) model fine-tuned on [ViEn-ESG-100](https://huggingface.co/nguyen599/ViEn-ESG-100) dataset, include 100,000 annotated sentences from Vietnam, English news and ESG reports.
+
+**Input**: A financial text.
+
+**Output**: Environmental, Social, Governance or Neural.
+
+**Language support**: English, Vietnamese
+
+# How to use
+You can use this model with Transformers pipeline for ESG classification or fill mask task.
+```python
+# tested in transformers==4.53.0
+from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
+
+maskesg = AutoModelForMaskedLM.from_pretrained('nguyen599/MaskESG-DistilBERT-base')
+tokenizer = AutoTokenizer.from_pretrained('nguyen599/MaskESG-DistilBERT-base')
+nlp = pipeline("fill-mask", model=maskesg, tokenizer=tokenizer)
+# Classification as fill-mask
+results = nlp(f'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is {tokenizer.mask_token}')
+print(results)
+# [{'score': 0.9015821814537048,
+#   'token': 444,
+#   'token_str': ' E',
+#   'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is E'},
+#  {'score': 0.09723947197198868,
+#   'token': 427,
+#   'token_str': ' N',
+#   'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is N'},
+#  {'score': 0.0010556845227256417,
+#   'token': 322,
+#   'token_str': ' S',
+#   'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is S'},
+#  {'score': 0.0001152529803221114,
+#   'token': 443,
+#   'token_str': ' G',
+#   'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is G'},
+#  {'score': 1.14425779429439e-06,
+#   'token': 299,
+#   'token_str': ' e',
+#   'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is e'}]
+
+```

model.safetensors

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