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--- |
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language: |
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- en |
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widget: |
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- text: >- |
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Extract lots from given text. |
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1. Age 18 to 75 years, inclusive. 2. Study participants must have a |
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diagnosis of symptomatic multiple myeloma requiring systemic therapy and are |
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eligible for the planned ASCT. 3. Untreated bone marrow sample was shipped |
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to Princess Margaret Hospital for MRD assay. 4. Must have been treated with |
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a velcade-based induction regimen. No limit to the number of cycles of |
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induction. 5. Study participants in whom the minimum stem cell dose of 2.0 x |
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106 cluster of differentiation (CD)34+ cells/kg has been collected. 6. |
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Eastern Cooperative Oncology Group (ECOG) Performance Status of 0-2. 7. |
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Negative beta-human chorionic gonadotropin (β-HCG) pregnancy test in all |
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females of child-bearing potential (FOCBP). 8. Ability to provide written |
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informed consent prior to initiation of any study-related procedures, and |
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ability, in the opinion of the Principal Investigator, to comply with all |
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requirements of the study. |
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example_title: Translation |
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- text: >- |
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Extract lots from given text. |
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age ≥18 years * patients with de novo or secondary AML, with an unfavorable |
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or intermediate karyotype (according to the 2017 ELN classification), or |
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patients with relapsing AML who may receive second-line treatment * not |
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candidates for intensive induction, for the following reasons* 75 years or ≥ |
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18 to 74 years and at least one of the following comorbidities: PS ≥ 2 or a |
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history of heart failure requiring treatment or LVEF ≤ 50% or chronic stable |
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angina or FEV1 ≤ 65% or DLCO ≤ 65% or creatinine clearance <45 ml / min; or |
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liver damage with total bilirubin> 1.5 N or other comorbidities that the |
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hematologist considers incompatible with intensive treatment * ineligible |
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for a classic allogeneic hematopoietic stem cell transplant due to the |
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presence of co-morbidities or too high a risk of toxicity >70 years old or |
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at least one of the following comorbidities: PS ≥ 2 or a history of heart |
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failure requiring treatment or LVEF ≤ 50% or chronic stable angina or FEV1 ≤ |
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65% or DLCO ≤ 65% or creatinine clearance <45 ml / min; or liver damage with |
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total bilirubin> 1.5 N * may receive chemotherapy with hypomethylating |
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agents have a partially compatible (haplo-identical) major family donor (≥18 |
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years old) eligible for lymphocyte donation. |
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example_title: Example 2 |
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library_name: transformers |
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tags: |
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- lot |
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- line of therapy |
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license: apache-2.0 |
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pipeline_tag: text2text-generation |
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base_model: google-t5/t5-small |
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--- |
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## Uses |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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tokenizer = T5Tokenizer.from_pretrained("alpeshsonar/lot-t5-small-filter") |
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model = T5ForConditionalGeneration.from_pretrained("alpeshsonar/lot-t5-small-filter") |
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input_text = """"Extract lots from given text. |
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* age ≥18 years * patients with de novo or secondary AML, with an unfavorable or intermediate karyotype (according to the 2017 ELN classification), or patients with relapsing AML who may receive second-line treatment * not candidates for intensive induction, for the following reasons* 75 years or ≥ 18 to 74 years and at least one of the following comorbidities: PS ≥ 2 or a history of heart failure requiring treatment or LVEF ≤ 50% or chronic stable angina or FEV1 ≤ 65% or DLCO ≤ 65% or creatinine clearance <45 ml / min; or liver damage with total bilirubin> 1.5 N or other comorbidities that the hematologist considers incompatible with intensive treatment * ineligible for a classic allogeneic hematopoietic stem cell transplant due to the presence of co-morbidities or too high a risk of toxicity >70 years old or at least one of the following comorbidities: PS ≥ 2 or a history of heart failure requiring treatment or LVEF ≤ 50% or chronic stable angina or FEV1 ≤ 65% or DLCO ≤ 65% or creatinine clearance <45 ml / min; or liver damage with total bilirubin> 1.5 N * may receive chemotherapy with hypomethylating agents have a partially compatible (haplo-identical) major family donor (≥18 years old) eligible for lymphocyte donation. |
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""" |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids,max_new_tokens=1024) |
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print(tokenizer.decode(outputs[0],skip_special_tokens=True)) |
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``` |
