Comments (2)
This is highly required as training the model is time taking and should be avoided as much as possible. I tested NER with PERSON Dataset of my own, which contains 6.4Lakh names and it takes close to 30 seconds to produce result. This can be reduced if pretrained models are present.
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Implemented it: #104
Will be released in 1.5
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Related Issues (20)
- EMRFS Pretrained Model Cache HOT 4
- Add support MegaTron based transformers
- MultiDateMatcher only returning 1 element
- Spark-nlp python return only first date in sentence
- .setReadMonthFirst always reads month first HOT 1
- .setReadMonthFirst always reads month first
- .setReadMonthFirst always reads month first
- SparkSession should only be created and accessed on the driver HOT 2
- Unclear in general how to use classification models requiring sentence pairs
- ONNX models crash when they are used in Colab's T4 GPU runtime HOT 3
- No Operation named [init_all_tables] in the Graph when using GPT2Transformer HOT 4
- NER pipeline Not scaling up to use the full cluster nodes HOT 3
- Unsupported Model: BGEEmbeddings
- Spark NLP Configuration's spark.jsl.settings.storage.cluster_tmp_dir: Databricks DBFS location does not work HOT 3
- spark-nlp in databricks writing to root s3 in cluster HOT 1
- Import Whisper large v3 into Spark NLP HOT 5
- Zero-Shot NER gives wrong entities with labels HOT 12
- Cannot cast to float HOT 2
- Flexible normalization HOT 1
- XlmRoBertaSentenceEmbeddings returns huge amount of embeddings instead of set dimensions
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