diff --git a/model_cards/gsarti/scibert-nli/README.md b/model_cards/gsarti/scibert-nli/README.md
index 1388fea8..661d0445 100644
--- a/model_cards/gsarti/scibert-nli/README.md
+++ b/model_cards/gsarti/scibert-nli/README.md
@@ -2,26 +2,28 @@
 
 This is the model [SciBERT](https://github.com/allenai/scibert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
 
-The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**. 
+The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
 
-**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace AutoModel
+**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace's `AutoModel`.
+
+**Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
 
 **Parameters**:
 
-| Parameter      | Value |
-|----------------|-------|
-| Batch size     | 64    |
-| Training steps | 20000 |
-| Warmup steps   | 1450  |
+| Parameter        | Value |
+|------------------|-------|
+| Batch size       | 64    |
+| Training steps   | 20000 |
+| Warmup steps     | 1450  |
+| Lowercasing      | True  |
+| Max. Seq. Length | 128   |
 
 **Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
 
-
-| Model                       | Score       |
-|-----------------------------|-------------|
-| `scibert-nli` (ours)        | 74.50       |
-| `bert-base-nli-mean-tokens` | 77.12       |
-
+| Model                         | Score       |
+|-------------------------------|-------------|
+| `scibert-nli` (this)          | 74.50       |
+| `bert-base-nli-mean-tokens`[3]| 77.12       |
 
 An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
 
@@ -30,3 +32,5 @@ An example usage for similarity-based scientific paper retrieval is provided in
 [1] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
 
 [2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
+
+[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)