Minor

README.md

@@ -51,6 +51,7 @@ for score in results:
 
 Sem-F1 also accepts multiple optional arguments:
 
+
 - `model_type (str)`: Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
 
     - `pv1` - [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1)

encoder_models.py

@@ -4,7 +4,7 @@ from typing import List, Union
 from numpy.typing import NDArray
 from sentence_transformers import SentenceTransformer
 
-from type_aliases import ENCODER_DEVICE_TYPE
+from .type_aliases import ENCODER_DEVICE_TYPE
 
 
 class Encoder(abc.ABC):

semf1.py

@@ -23,9 +23,9 @@ import numpy as np
 from numpy.typing import NDArray
 from sklearn.metrics.pairwise import cosine_similarity
 
-from encoder_models import get_encoder
-from type_aliases import DEVICE_TYPE, PREDICTION_TYPE, REFERENCE_TYPE
-from utils import is_nested_list_of_type, Scores, slice_embeddings, flatten_list, get_gpu
+from .encoder_models import get_encoder
+from .type_aliases import DEVICE_TYPE, PREDICTION_TYPE, REFERENCE_TYPE
+from .utils import is_nested_list_of_type, Scores, slice_embeddings, flatten_list, get_gpu
 
 _CITATION = """\
 @inproceedings{bansal-etal-2022-sem,
@@ -410,3 +410,4 @@ class SemF1(evaluate.Metric):
             results.append(Scores(precision, recall_scores))
 
         return results
+

utils.py

@@ -6,7 +6,7 @@ from typing import List, Union
 import torch
 from numpy.typing import NDArray
 
-from type_aliases import DEVICE_TYPE, ENCODER_DEVICE_TYPE, NumSentencesType, EmbeddingSlicesType
+from .type_aliases import DEVICE_TYPE, ENCODER_DEVICE_TYPE, NumSentencesType, EmbeddingSlicesType
 
 
 def get_gpu(gpu: DEVICE_TYPE) -> ENCODER_DEVICE_TYPE: