---
language: 
- en
widget:
- text: "Extract lots from given text.\n1. Age 18 to 75 years, inclusive. 2. Study participants must have a diagnosis of symptomatic multiple myeloma requiring systemic therapy and are eligible for the planned ASCT. 3. Untreated bone marrow sample was shipped to Princess Margaret Hospital for MRD assay. 4. Must have been treated with a velcade-based induction regimen. No limit to the number of cycles of induction. 5. Study participants in whom the minimum stem cell dose of 2.0 x 106 cluster of differentiation (CD)34+ cells/kg has been collected. 6. Eastern Cooperative Oncology Group (ECOG) Performance Status of 0-2. 7. Negative beta-human chorionic gonadotropin (β-HCG) pregnancy test in all females of child-bearing potential (FOCBP). 8. Ability to provide written informed consent prior to initiation of any study-related procedures, and ability, in the opinion of the Principal Investigator, to comply with all requirements of the study."
  example_title: "Translation"
- text: "Extract lots from given text.\nage ≥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."
  example_title: "Example 2"
library_name: transformers
tags:
- lot
- line of therapy
license: apache-2.0
pipeline_tag: text2text-generation
---
## Uses

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("alpeshsonar/lot-t5-small-filter")
model = T5ForConditionalGeneration.from_pretrained("alpeshsonar/lot-t5-small-filter")

input_text = """"Extract lots from given text.
* 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.
"""
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids,max_new_tokens=1024)
print(tokenizer.decode(outputs[0],skip_special_tokens=True))
```